@article{
 title = {Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging},
 type = {article},
 year = {2025},
 websites = {https://doi.org/10.1007/s11517-024-03264-z},
 id = {f3f6f5de-53e0-3e9c-884a-3bf2aeab78e5},
 created = {2025-01-11T20:28:36.389Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2025-01-11T20:28:36.389Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.},
 bibtype = {article},
 author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro J and Peluffo-Ordoñez, Diego H},
 doi = {10.1007/s11517-024-03264-z},
 journal = {Medical & Biological Engineering & Computing}
}

This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.

@article{
 title = {Advances in Electroencephalography for Post-Traumatic Stress Disorder Identification: A Scoping Review},
 type = {article},
 year = {2025},
 keywords = {Biomarkers,Brain,Brain electrical activity analysis,Brain modeling,Electroencephalography,Machine learning,Medical treatment,Mental health,Reviews,Sleep,Technological innovation,diagnosis and therapy,electroencephalography,machine learning,post-traumatic stress disorder},
 pages = {332-344},
 volume = {6},
 websites = {https://ieeexplore.ieee.org/document/10874198},
 id = {21754603-4fea-30ee-a99b-d830485cef98},
 created = {2025-03-27T17:58:09.191Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2025-03-27T17:58:09.191Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10874198},
 source_type = {article},
 private_publication = {false},
 abstract = {Background: Post-traumatic stress disorder (PTSD) is a psychophysiological condition caused by traumatic experiences. Its diagnosis typically relies on subjective tools like clinical interviews and self-reports. Objectives: This scoping review analyzes computational methods using EEG signal processing for PTSD diagnosis, differentiation, and therapy. It provides a comprehensive overview of the entire EEG analysis pipeline, from acquisition to statistical and machine learning techniques for PTSD diagnosis. Methods: Using the PRISMA-ScR protocol, studies published between 2013 and 2024 were reviewed from databases including Scopus, Web of Science, and PubMed. A total of 73 studies were analyzed: 52 on diagnosis, 8 on differentiation, and 15 on therapy. Results: EEG Bands and Event-Related Potentials (ERP) were the dominant techniques. The Alpha band demonstrated strong performance in diagnosis and therapy. LPP ERP was most effective for diagnosis, and P300 for differentiation. Supervised SVM models achieved the highest accuracy in diagnosis (ACC = 0.997), differentiation (ACC = 0.841), and psychotherapy (ACC = 0.78). Random Forest multimodal models integrating EEG with other modalities (e.g., ECG, GSR, Speech) achieved ACC = 0.993. Unsupervised approach is employed to cluster patients to identify PTSD subtypes or to differentiate PTSD from other mental disorders. Veterans and combatants were the primary study population, and only three studies reported open datasets. Conclusions: EEG-based methods hold promise as objective tools for PTSD diagnosis and therapy. The review identified limitations in the use of ERP, sleep characterization and full-band EEG. Broader datasets representing diverse populations are essential to mitigate bias and facilitate robust inter-model comparisons. Future research should focus on deep learning, adaptive signal decomposition, and multimodal approaches.},
 bibtype = {article},
 author = {Salazar-Castro, José A and Peluffo-Ordóñez, Diego H and López, Diego M},
 doi = {10.1109/OJEMB.2025.3538498},
 journal = {IEEE Open Journal of Engineering in Medicine and Biology}
}

Background: Post-traumatic stress disorder (PTSD) is a psychophysiological condition caused by traumatic experiences. Its diagnosis typically relies on subjective tools like clinical interviews and self-reports. Objectives: This scoping review analyzes computational methods using EEG signal processing for PTSD diagnosis, differentiation, and therapy. It provides a comprehensive overview of the entire EEG analysis pipeline, from acquisition to statistical and machine learning techniques for PTSD diagnosis. Methods: Using the PRISMA-ScR protocol, studies published between 2013 and 2024 were reviewed from databases including Scopus, Web of Science, and PubMed. A total of 73 studies were analyzed: 52 on diagnosis, 8 on differentiation, and 15 on therapy. Results: EEG Bands and Event-Related Potentials (ERP) were the dominant techniques. The Alpha band demonstrated strong performance in diagnosis and therapy. LPP ERP was most effective for diagnosis, and P300 for differentiation. Supervised SVM models achieved the highest accuracy in diagnosis (ACC = 0.997), differentiation (ACC = 0.841), and psychotherapy (ACC = 0.78). Random Forest multimodal models integrating EEG with other modalities (e.g., ECG, GSR, Speech) achieved ACC = 0.993. Unsupervised approach is employed to cluster patients to identify PTSD subtypes or to differentiate PTSD from other mental disorders. Veterans and combatants were the primary study population, and only three studies reported open datasets. Conclusions: EEG-based methods hold promise as objective tools for PTSD diagnosis and therapy. The review identified limitations in the use of ERP, sleep characterization and full-band EEG. Broader datasets representing diverse populations are essential to mitigate bias and facilitate robust inter-model comparisons. Future research should focus on deep learning, adaptive signal decomposition, and multimodal approaches.

@article{
 title = {Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality},
 type = {article},
 year = {2025},
 volume = {15},
 websites = {https://www.mdpi.com/2076-3417/15/7/3665},
 id = {f08e0b69-5448-3e0a-9d31-051d788aafef},
 created = {2025-03-28T20:30:17.830Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2025-03-28T20:30:17.830Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {app15073665},
 source_type = {article},
 private_publication = {false},
 abstract = {Persistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application.},
 bibtype = {article},
 author = {Becerra, Miguel A and Peluffo-Ordoñez, Diego H and Vela, Johana and Mejía, Cristian and Ugarte, Juan P and Tobón, Catalina},
 doi = {10.3390/app15073665},
 journal = {Applied Sciences},
 number = {7}
}

Persistent atrial fibrillation (AF), a prevalent cardiac arrhythmia, is primarily sustained by rotor-type reentries, with their localization crucial for successful ablation treatment. Fractionated atrial electrogram (EGM) signals have been associated with the tips of the rotors and are thus considered as ablation targets. However, the typical noise problems of physiological signals affect the results of EGM processing tools, and consequently the ablation outcome. This study proposes a data fusion framework based on the Joint Directors of Laboratories model with six levels and information quality (IQ) assessment for locating rotor tips from EGMs simulated in a two-dimensional model of human atrial tissue under AF conditions. Validation tests were conducted using a set of 13 IQ criteria and their corresponding metrics. First, EGMs were contaminated with different types of noise and artifacts (power-line interference, spikes, loss of samples, and loss of resolution) to assess tolerance. The signals were then preprocessed, and five statistical features (sample entropy, approximate entropy, Shannon entropy, mean amplitude, and standard deviation) were extracted to generate rotor location maps using a wavelet fusion technique. Fuzzy inference was applied for situation and risk assessment, followed by IQ mapping using a support vector machine by level. Finally, the IQ criteria were optimized through a particle swarm optimization algorithm. The proposed framework outperformed existing EGM-based rotor detection methods, demonstrating superior functionality and performance compared to existing EGM-based rotor detection methods. It achieved an accuracy of approximately 90%, with improvements of up to 10% through tuning and adjustments based on IQ variables, aligned with higher-level system requirements. The novelty of this approach lies in evaluating the IQ across signal-processing stages and optimizing it through data fusion to enhance rotor tip position estimation. This advancement could help specialists make more informed decisions in EGM acquisition and treatment application.

@inproceedings{
 title = {Recognition and Classification of Cardiac Arrhythmias Using Discrete Wavelet Transform (DWT) and Machine Learning Techniques},
 type = {inproceedings},
 year = {2023},
 pages = {3-15},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_1},
 publisher = {Springer Nature Switzerland},
 city = {Cham},
 id = {42fd7735-46cc-3ce7-aa8f-da1fe58102b3},
 created = {2023-02-13T23:09:19.820Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2023-02-13T23:09:19.820Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-25942-5_1},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Cardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. Therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. Therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters; the location of the QRS complex is essential for the identification of the ECG signal. Therefore, the position and amplitude of the R peaks are determined in the segmentation stage. Then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). The ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. These methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54\% using the k-Nearest Neighbors classifier.},
 bibtype = {inproceedings},
 author = {Ayala-Cucas, Hermes Andrés and Mora-Piscal, Edison Alexander and Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},
 editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},
 booktitle = {Trends in Artificial Intelligence and Computer Engineering}
}

Cardiac arrhythmias are heart rhythm problems that usually occur when the electrical impulses coordinated with the heartbeat do not work correctly. For this reason, detecting abnormalities in an electrocardiogram (ECG) plays a vital role in patient follow-up. Due to the presence of noise, the irregularity of the heartbeat, and the nonstationary nature of ECG signals, their interpretation can be difficult, requiring the use of advanced computer systems to support the diagnosis of cardiac disorders. Therefore, the development of assisted ECG analysis systems is a current topic of study, and the main challenge is to achieve adequate accuracy for application in the clinical setting. Therefore, this article describes a software tool for classifying ECG samples into the main classes of cardiac arrhythmias by removing noise from the ECG signal at the preprocessing stage using conventional digital filters; the location of the QRS complex is essential for the identification of the ECG signal. Therefore, the position and amplitude of the R peaks are determined in the segmentation stage. Then the selection of the most relevant features of the ECG signal is performed using the discrete wavelet transform (DWT). The ability of the extracted features to differentiate between different classes of data is tested using machine learning techniques such as k-Nearest Neighbors, Neural Networks, and Decision Trees with 10-fold cross-validation. These methods are evaluated and tested with the MIT-BIH arrhythmia database, achieving the best accuracy of 98.54\% using the k-Nearest Neighbors classifier.

@inproceedings{
 title = {Neural Networks on Noninvasive Electrocardiographic Imaging Reconstructions: Preliminary Results},
 type = {inproceedings},
 year = {2023},
 pages = {55-63},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-25942-5_5},
 publisher = {Springer Nature Switzerland},
 city = {Cham},
 id = {7aa9d466-965a-37c8-83aa-63866c03af68},
 created = {2023-02-13T23:42:39.398Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2023-02-13T23:42:39.398Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-25942-5_5},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).},
 bibtype = {inproceedings},
 author = {Mayorca-Torres, Dagoberto and León-Salas, Alejandro José and Peluffo-Ordoñez, Diego Hernán},
 editor = {Botto-Tobar, Miguel and Gómez, Omar S and Rosero Miranda, Raul and Díaz Cadena, Angela and Luna-Encalada, Washington},
 booktitle = {Trends in Artificial Intelligence and Computer Engineering}
}

In the reverse electrocardiography (ECG) problem, the objective is to reconstruct the heart's electrical activity from a set of body surface potentials by solving the direct model and the geometry of the torso. Over the years, researchers have used various approaches to solve this problem, from direct, iterative, probabilistic, and those based on deep learning. The interest of the latter, among the wide range of techniques, is because the complexity of the problem can be significantly reduced while increasing the precision of the estimation. In this article, we evaluate the performance of a deep learning-based neural network compared to the Tikhonov method of zero order (ZOT), first (FOT), and second (SOT). Preliminary results show an improvement in performance over real data when Pearson's correlation coefficient (CC) and (RMSE) are calculated. The CC's mean value and standard deviation for the proposed method were 0.960 (0.065), well above ZOT, which was 0.864 (0.047).

@article{
 title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks},
 type = {article},
 year = {2023},
 keywords = {Brain–computer interface,Electroencephalography,Experimental methodology,Motor imagery task,Motor task,OpenBCI},
 pages = {109540},
 volume = {50},
 websites = {https://www.sciencedirect.com/science/article/pii/S2352340923006406},
 id = {04da746d-9108-396f-88b2-d3519b188b96},
 created = {2023-10-02T16:29:47.316Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2023-10-02T16:29:47.316Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {ASANZA2023109540},
 source_type = {article},
 private_publication = {false},
 abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.},
 bibtype = {article},
 author = {Asanza, Víctor and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H and Montoya, Daniel and Gonzalez, Kleber},
 doi = {https://doi.org/10.1016/j.dib.2023.109540},
 journal = {Data in Brief}
}

Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.

@article{
 title = {Myoelectric Prosthesis Using Sensor Fusion Between Electromyography and Pulse Oximetry Signals},
 type = {article},
 year = {2023},
 keywords = {artificial,bioelectric signal,electromyography,intelligence,myoelectric prosthesis,neural network,sensor fusion},
 pages = {641-649},
 volume = {56},
 websites = {https://www.iieta.org/journals/jesa/paper/10.18280/jesa.560413},
 id = {d3f42bae-61cb-3fd9-97cf-a2eb338d41c2},
 created = {2023-10-02T16:29:47.320Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2023-10-02T16:29:47.320Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {TorresK.EspinozaJ.AsanzaV.Lorente-LeyvaL.L.Peluffo-Ordonez2023},
 private_publication = {false},
 abstract = {Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.},
 bibtype = {article},
 author = {Torres, K., Espinoza, J., Asanza, V., Lorente-Leyva, L.L., Peluffo-Ordóñez, D.H.},
 journal = {Journal Européen des Systèmes Automatisés},
 number = {4}
}

Approximately 215,156 people in Ecuador grapple with physical disabilities, of whom nearly half fall within the 30 to 49% disability range, and a considerable number lack limbs. Moreover, there's been a surge in amputation cases, a trend linked to the increasing diabetes prevalence estimated at around 537 million cases by 2021 as per the International Diabetes Federation (IDF). While prosthetic solutions exist, they might incur high costs or offer constrained movement, even when more affordable. Thus, an alternative is proposed: a myoelectric upper limb prosthesis. This prosthesis would be maneuvered through electromyography and pulse oximetry signals, leveraging artificial intelligence methods. Employing a multi-layer neural network model, a structure comprising an input layer, four hidden layers, and an output layer, yields an impressive 93% prediction accuracy for user movement intentions. For AI model training, data from EMG and PPG sensors were recorded and scrutinized, leading to the condensation of classes from four to three. The model was embedded within an ESP32 C3 DevKit-M1 development board, and open-source blueprints facilitated the prosthesis's creation, complemented by supplementary components for electronics integration. The model attains a 93% precision in predicting classes, while the prosthesis's endurance spans approximately three hours and costs $295, equipped to handle diverse lightweight objects.

@article{
 title = {[Methodology for biometric identification based on EEG signals in multiple emotional states] Metodología para la identificación biométrica a partir de señales EEG en múltiples estados emocionales},
 type = {article},
 year = {2023},
 pages = {281-288},
 websites = {https://www.proquest.com/docview/2880949468?fromopenview=true&pq-origsite=gscholar},
 publisher = {Associação Ibérica de Sistemas e Tecnologias de Informacao},
 id = {c3070297-3e25-3c6e-806b-f6d4fe3c0d8f},
 created = {2023-11-02T20:55:18.746Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2024-05-26T22:13:27.788Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {duque2023metodologia},
 source_type = {article},
 private_publication = {false},
 abstract = {Biometric identification is in constant development as well as the systems that violate it, so it is an open field of research that requires new analysis and application of techniques to identify its vulnerabilities and improve its reliability levels. In this work we propose a biometric identification system based on EEG signals in multiple emotional states considering that the underlying dynamics of EEG signals vary according to the emotional state, which may affect the accuracy of classification models. El algoritmo de bosques aleatorios demostró la mayor exactitud (94%) superando los demás algoritmos. 2. Diferentes medidas fueron obtenidas para cada cada una de las señales EEG, a partir usando características derivadas de moving average, linear dynamic systems (LDS), rational asymmetry (RASM), computed differential symmetry (DASM), differential entropy (DE), differential caudality (DCAU), power spectral density (PSD), asymmetry (ASM).},
 bibtype = {article},
 author = {Duque-Mejía, Carolina and Castro, Andrés and Duque, Eduardo and Serna-Guarín, Leonardo and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego and Becerra, Miguel A},
 journal = {Revista Ibérica de Sistemas e Tecnologias de Informação},
 number = {E62}
}

Biometric identification is in constant development as well as the systems that violate it, so it is an open field of research that requires new analysis and application of techniques to identify its vulnerabilities and improve its reliability levels. In this work we propose a biometric identification system based on EEG signals in multiple emotional states considering that the underlying dynamics of EEG signals vary according to the emotional state, which may affect the accuracy of classification models. El algoritmo de bosques aleatorios demostró la mayor exactitud (94%) superando los demás algoritmos. 2. Diferentes medidas fueron obtenidas para cada cada una de las señales EEG, a partir usando características derivadas de moving average, linear dynamic systems (LDS), rational asymmetry (RASM), computed differential symmetry (DASM), differential entropy (DE), differential caudality (DCAU), power spectral density (PSD), asymmetry (ASM).

@inproceedings{
 title = {Parkinson's Disease Diagnosis Through Electroencephalographic Signal Processing and Sub-optimal Feature Extraction},
 type = {inproceedings},
 year = {2022},
 pages = {118-127},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-96293-7_12},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {da7573c3-3a4d-3756-b267-7ff0bd72e919},
 created = {2022-03-03T22:54:45.451Z},
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 citation_key = {10.1007/978-3-030-96293-7_12},
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 abstract = {Parkinson's disease is the second most common neurological disorder after Alzheimer. Several limitations and challenges have arisen when aiming to diagnose this disease. In this regard, a computer-aided diagnosis system is enforced for the early detection of any abnormalities. Prominent research efforts have been developed based on speech and gait analysis; nonetheless, electroencephalographic (EEG)-signal-driven approaches have acquired some interest recently to diagnose an early Parkinson's disease. According to recent studies, the angles and sharpness of brain waves may hold key hints to detect Parkinson's disease. In the present work, an exploratory study over digital signal processing, and machine learning techniques for characterizing and classifying Parkinson-diagnosed EEG signals is conducted; waveform shape, spectral, statistical and non-linear features are taken into account for the present study. The results, without being definitive, propose a suitable set of processing techniques to increase the performance, estimation accuracy, and interpretation of this physiological phenomenon. At the end, it was found that with the characterization performed, k-NN is the classifier which performs better, obtaining a mean accuracy of 86\% when differentiating Parkinson's disease patients and healthy control subjects.},
 bibtype = {inproceedings},
 author = {Pozo-Ruiz, Santiago and Morocho-Cayamcela, Manuel Eugenio and Mayorca-Torres, Dagoberto and H. Peluffo-Ordóñez, Diego},
 editor = {Rocha, Álvaro and Ferrás, Carlos and Méndez Porras, Abel and Jimenez Delgado, Efren},
 booktitle = {Information Technology and Systems}
}

Parkinson's disease is the second most common neurological disorder after Alzheimer. Several limitations and challenges have arisen when aiming to diagnose this disease. In this regard, a computer-aided diagnosis system is enforced for the early detection of any abnormalities. Prominent research efforts have been developed based on speech and gait analysis; nonetheless, electroencephalographic (EEG)-signal-driven approaches have acquired some interest recently to diagnose an early Parkinson's disease. According to recent studies, the angles and sharpness of brain waves may hold key hints to detect Parkinson's disease. In the present work, an exploratory study over digital signal processing, and machine learning techniques for characterizing and classifying Parkinson-diagnosed EEG signals is conducted; waveform shape, spectral, statistical and non-linear features are taken into account for the present study. The results, without being definitive, propose a suitable set of processing techniques to increase the performance, estimation accuracy, and interpretation of this physiological phenomenon. At the end, it was found that with the characterization performed, k-NN is the classifier which performs better, obtaining a mean accuracy of 86\% when differentiating Parkinson's disease patients and healthy control subjects.

@article{
 title = {Identification of Lower-Limb Motor Tasks via Brain-Computer Interfaces: A Topical Overview},
 type = {article},
 year = {2022},
 volume = {22},
 websites = {https://www.mdpi.com/1424-8220/22/5/2028},
 id = {dc5bb4e8-ca34-3e84-9732-143ce042a656},
 created = {2022-03-05T01:20:50.296Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-03-05T01:20:50.296Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {s22052028},
 source_type = {article},
 private_publication = {false},
 abstract = {Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.},
 bibtype = {article},
 author = {Asanza, Víctor and Peláez, Enrique and Loayza, Francis and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego H},
 doi = {10.3390/s22052028},
 journal = {Sensors},
 number = {5}
}

Recent engineering and neuroscience applications have led to the development of brain–computer interface (BCI) systems that improve the quality of life of people with motor disabilities. In the same area, a significant number of studies have been conducted in identifying or classifying upper-limb movement intentions. On the contrary, few works have been concerned with movement intention identification for lower limbs. Notwithstanding, lower-limb neurorehabilitation is a major topic in medical settings, as some people suffer from mobility problems in their lower limbs, such as those diagnosed with neurodegenerative disorders, such as multiple sclerosis, and people with hemiplegia or quadriplegia. Particularly, the conventional pattern recognition (PR) systems are one of the most suitable computational tools for electroencephalography (EEG) signal analysis as the explicit knowledge of the features involved in the PR process itself is crucial for both improving signal classification performance and providing more interpretability. In this regard, there is a real need for outline and comparative studies gathering benchmark and state-of-art PR techniques that allow for a deeper understanding thereof and a proper selection of a specific technique. This study conducted a topical overview of specialized papers covering lower-limb motor task identification through PR-based BCI/EEG signal analysis systems. To do so, we first established search terms and inclusion and exclusion criteria to find the most relevant papers on the subject. As a result, we identified the 22 most relevant papers. Next, we reviewed their experimental methodologies for recording EEG signals during the execution of lower limb tasks. In addition, we review the algorithms used in the preprocessing, feature extraction, and classification stages. Finally, we compared all the algorithms and determined which of them are the most suitable in terms of accuracy.

@article{
 title = {A New Data-Preprocessing-Related Taxonomy of Sensors for IoT Applications},
 type = {article},
 year = {2022},
 volume = {13},
 websites = {https://www.mdpi.com/2078-2489/13/5/241},
 id = {03d0d16c-f1aa-379e-9868-0f36e328668d},
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 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {info13050241},
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 abstract = {IoT devices play a fundamental role in the machine learning (ML) application pipeline, as they collect rich data for model training using sensors. However, this process can be affected by uncontrollable variables that introduce errors into the data, resulting in a higher computational cost to eliminate them. Thus, selecting the most suitable algorithm for this pre-processing step on-device can reduce ML model complexity and unnecessary bandwidth usage for cloud processing. Therefore, this work presents a new sensor taxonomy with which to deploy data pre-processing on an IoT device by using a specific filter for each data type that the system handles. We define statistical and functional performance metrics to perform filter selection. Experimental results show that the Butterworth filter is a suitable solution for invariant sampling rates, while the Savi–Golay and medium filters are appropriate choices for variable sampling rates.},
 bibtype = {article},
 author = {Rosero-Montalvo, Paul D and López-Batista, Vivian F and Peluffo-Ordóñez, Diego H},
 doi = {10.3390/info13050241},
 journal = {Information},
 number = {5}
}

IoT devices play a fundamental role in the machine learning (ML) application pipeline, as they collect rich data for model training using sensors. However, this process can be affected by uncontrollable variables that introduce errors into the data, resulting in a higher computational cost to eliminate them. Thus, selecting the most suitable algorithm for this pre-processing step on-device can reduce ML model complexity and unnecessary bandwidth usage for cloud processing. Therefore, this work presents a new sensor taxonomy with which to deploy data pre-processing on an IoT device by using a specific filter for each data type that the system handles. We define statistical and functional performance metrics to perform filter selection. Experimental results show that the Butterworth filter is a suitable solution for invariant sampling rates, while the Savi–Golay and medium filters are appropriate choices for variable sampling rates.

@inproceedings{
 title = {ECG-Based Heartbeat Classification for Arrhythmia Detection Using Artificial Neural Networks},
 type = {inproceedings},
 year = {2022},
 pages = {247-259},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-10450-3_20},
 publisher = {Springer International Publishing},
 city = {Cham},
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 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-10450-3_20},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\% of overall accuracy for each register while keeping the computational cost low.},
 bibtype = {inproceedings},
 author = {Cepeda, Eduardo and Sánchez-Pozo, Nadia N and Peluffo-Ordóñez, Diego H and González-Vergara, Juan and Almeida-Galárraga, Diego},
 editor = {Gervasi, Osvaldo and Murgante, Beniamino and Hendrix, Eligius M T and Taniar, David and Apduhan, Bernady O},
 booktitle = {Computational Science and Its Applications -- ICCSA 2022}
}

Cardiovascular disease (CVD) has quickly grown in prevalence over the previous decade, becoming the major cause of human morbidity on a global scale. Due to the massive number of ECG data, manual analysis is regarded as a time-consuming, costly and prone to human error task. In the other hand, computational systems based on biomedical signal processing and machine learning techniques might be suited for supporting arrhythmia diagnostic processes, while solving some of those issues. In general, such systems involve five stages: acquisition, preprocessing, segmentation, characterization, and classification. Yet numerous fundamental aspects remain unresolved, including sensitivity to signal fluctuation, accuracy, computing cost, generalizability, and interpretability. In this context, the present study offers a comparative analysis of ECG signal classification using two artificial neural networks created by different machine learning frameworks. The neural nets were built into a pipeline that aims to strike an appropriate balance among signal robustness, variability, and accuracy. The proposed approach reaches up to 99\% of overall accuracy for each register while keeping the computational cost low.

@article{
 title = {Heart Rate Detection using a Piezoelectric Ceramic Sensor: Preliminary results},
 type = {article},
 year = {2022},
 volume = {7},
 websites = {http://revistabionatura.com/2022.07.03.30.html},
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 author = {E Cepeda, undefined and Peluffo-Ordóñez, D H and Rosero-Montalvo, P and Becerra, M A and Umaquinga-Criollo, A C and Ramírez., L},
 doi = {10.21931/RB/2022.07.03.30},
 journal = {Bionatura},
 number = {30}
}

@article{
 title = {Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach},
 type = {article},
 year = {2022},
 volume = {7},
 websites = {https://www.ijimai.org/journal/bibcite/reference/3162},
 id = {6629a9e1-5d56-3784-9647-dd4349c28c57},
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 author = {Proaño-Guevara, Daniel and Blanco-Valencia, Xiomara and Rosero-Montalvo, Paul D and Peluffo-Ordóñez, Diego H},
 doi = {10.9781/ijimai.2022.08.009},
 journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
 number = {5}
}

@inproceedings{
 title = {Electrooculography Signals Classification for FPGA-based Human-Computer Interaction},
 type = {inproceedings},
 year = {2022},
 pages = {1-7},
 websites = {https://ieeexplore.ieee.org/document/9989664},
 id = {4d87e654-4933-3f4c-9826-f3537fce080b},
 created = {2022-12-28T23:00:00.597Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-12-28T23:00:00.597Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {9989664},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.},
 bibtype = {inproceedings},
 author = {Asanza, Víctor and Miranda, Jesús and Miranda, Jocelyn and Rivas, Leiber and Hernan Peluffo-Ordóñez, Diego and Pelaez, Enrique and Loayza, Francis and Alejandro, Otilia},
 doi = {10.1109/ANDESCON56260.2022.9989664},
 booktitle = {2022 IEEE ANDESCON}
}

Electrooculographic techniques are applied in the development of new technologies that compensate for the limitations of people with motor disabilities. The algorithms in charge of classifying these signals play a fundamental role, mainly for Human Computer Interfaces (HCI), specially when the machine learning algorithms are implemented in customized hardware like FPGA. In this work, electrooculography data were collected from 10 healthy subjects during six eye movement tasks. Then, the data were filtered and introduced into supervised and unsupervised learning algorithms with six classification labels. The results obtained showed that the SVM algorithm had 93.5% of accuracy, thus being considered the most efficient of the classification algorithms proposed in this work. Then, we develop a custom hardware architecture for real-time implementation of EOG classification model in al FPGA card. We demonstrate the effectiveness of the proposed framework for EOG data classification.

@inproceedings{
 title = {Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques},
 type = {inproceedings},
 year = {2022},
 pages = {27-40},
 websites = {https://link.springer.com/chapter/10.1007/978-3-031-22419-5_3},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {55f0e650-d212-36c2-8f79-fc65e6955b2f},
 created = {2023-01-04T00:03:18.527Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2023-01-04T00:03:18.527Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-031-22419-5_3},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart's electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.},
 bibtype = {inproceedings},
 author = {Andrés Ayala-Cucas, Hermes and Mora-Piscal, Edison Alexander and Mayorca-Torres, Dagoberto and Peluffo-Ordoñez, Diego Hernán and León-Salas, Alejandro J},
 editor = {Bicharra Garcia, Ana Cristina and Ferro, Mariza and Rodríguez Ribón, Julio Cesar},
 booktitle = {Advances in Artificial Intelligence -- IBERAMIA 2022}
}

Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart's electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.

@article{
 title = {Design of a low computational cost prototype for cardiac arrhythmia detection [Diseño de un prototipo de bajo coste computacional para detección de arritmias cardiacas]},
 type = {article},
 year = {2021},
 pages = {470-479},
 volume = {2021},
 websites = {https://search.proquest.com/openview/d9dffd8a726c99f54a47adaf372e13b8/1?pq-origsite=gscholar&cbl=1006393},
 id = {099e35c3-bcc3-39b1-bdc5-ce3e01d19a7f},
 created = {2022-02-02T07:00:21.540Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:21.540Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Vargas-Muñoz2021470},
 source_type = {article},
 private_publication = {false},
 abstract = {This research presents the design of a prototype for the detection of cardiac arrhythmias that incorporates an embedded low-cost computational system in an environment of limited computational resources capable of analyzing characteristics of the QRS complexes. To do this, a strategy for classifying normal and pathological heart beats is developed in long-term electrocardiographic recordings (Holter), which are representative waves of the beat and their analysis allows identifying ventricular arrhythmias. For the development of this initial prototype, it is found that the use of the k nearest neighbors (k-NN) algorithm together with a stage of selection of variables from the training set is a good alternative and represents an important contribution of this work to experimental level. The experiments were carried out on the basis of cardiac arrhythmia data from the Massachusetts Institute of Technology (MIT). The results are satisfactory and promising. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.},
 bibtype = {article},
 author = {Vargas-Muñoz, A M and Chamorro-Sangoquiza, D C and Umaquinga-Criollo, A C and Rosero-Montalvo, P D and Becerra, M A and Peluffo-Ordóñez, D H and Revelo-Fuelagán, E J},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao},
 number = {E40}
}

This research presents the design of a prototype for the detection of cardiac arrhythmias that incorporates an embedded low-cost computational system in an environment of limited computational resources capable of analyzing characteristics of the QRS complexes. To do this, a strategy for classifying normal and pathological heart beats is developed in long-term electrocardiographic recordings (Holter), which are representative waves of the beat and their analysis allows identifying ventricular arrhythmias. For the development of this initial prototype, it is found that the use of the k nearest neighbors (k-NN) algorithm together with a stage of selection of variables from the training set is a good alternative and represents an important contribution of this work to experimental level. The experiments were carried out on the basis of cardiac arrhythmia data from the Massachusetts Institute of Technology (MIT). The results are satisfactory and promising. © 2021, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.

@inproceedings{
 title = {Sign Language Recognition Using Leap Motion Based on Time-Frequency Characterization and Conventional Machine Learning Techniques},
 type = {inproceedings},
 year = {2021},
 pages = {55-67},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-89654-6_5},
 publisher = {Springer International Publishing},
 city = {Cham},
 id = {54392e3b-7776-3b04-820d-375618a9a0d3},
 created = {2022-02-02T07:00:21.989Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:21.989Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {10.1007/978-3-030-89654-6_5},
 source_type = {inproceedings},
 private_publication = {false},
 abstract = {The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\% rating yield using the proposed methodology with the Fast Tree algorithm.},
 bibtype = {inproceedings},
 author = {López-Albán, D and López-Barrera, A and Mayorca-Torres, D and Peluffo-Ordóñez, D},
 editor = {Florez, Hector and Pollo-Cattaneo, Ma Florencia},
 booktitle = {Applied Informatics}
}

The abstract should briefly summarize the contents of the paper in Sign language is the form of communication between the deaf and hearing population, which uses the gesture-spatial configuration of the hands as a communication channel with their social environment. This work proposes the development of a gesture recognition method associated with sign language from the processing of time series from the spatial position of hand reference points granted by a Leap Motion optical sensor. A methodology applied to a validated American Sign Language (ASL) Dataset which involves the following sections: (i) preprocessing for filtering null frames, (ii) segmentation of relevant information, (iii) time-frequency characterization from the Discrete Wavelet Transform (DWT). Subsequently, the classification is carried out with Machine Learning algorithms (iv). It is graded by a 97.96\% rating yield using the proposed methodology with the Fast Tree algorithm.

@article{
 title = {Classification of Subjects with Parkinson’s Disease using Finger Tapping Dataset},
 type = {article},
 year = {2021},
 keywords = {Classification,Finger Tapping,Machine Learning,Parkinson’s disease},
 pages = {376-381},
 volume = {54},
 websites = {https://www.sciencedirect.com/science/article/pii/S2405896321016906},
 id = {a405d2bd-9c71-3bda-919c-330d1e5a1cce},
 created = {2022-02-02T07:00:22.263Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:22.263Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {ASANZA2021376},
 source_type = {article},
 private_publication = {false},
 abstract = {Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.},
 bibtype = {article},
 author = {Asanza, Víctor and Sánchez-Pozo, Nadia N and Lorente-Leyva, Leandro L and Peluffo-Ordóñez, Diego Hernan and Loayza, Fancis R and Peláez, Enrique},
 doi = {https://doi.org/10.1016/j.ifacol.2021.10.285},
 journal = {IFAC-PapersOnLine},
 number = {15}
}

Parkinson’s disease is the second most common neurodegenerative disorder and affects more than 7 million people globally. In this work, we classify subjects with Parkinson’s disease using data from finger-tapping on a keyboard. We use a free database by Physionet with more than 9 million records, preprocessed to delete atypical data. In the feature extraction stage, we obtained 48 features. We use Google Colaboratory to train, validate, and test nine supervised learning algorithms that detect the disease. As a result, we achieve a degree of accuracy higher than 98%.

@article{
 title = {Evaluation of characterization techniques for classification of seismic-volcanic signals of the nevado del ruiz},
 type = {article},
 year = {2020},
 keywords = {Cepstral,Characterization,Classification,Machine Learning,Seismic-volcanic},
 websites = {https://search.proquest.com/docview/2350120798},
 id = {9883888d-6e9d-3efc-aacf-6ffd4f8bdf9d},
 created = {2022-02-02T07:00:22.534Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:22.534Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Bravo2020},
 private_publication = {false},
 abstract = {Currently, researches have been carried out on automatic classification of seismic-volcanic events-mainly based on machine learning techniques-aimed at identifying the nature of the recorded event. In this sense, several approaches have been introduced. Nonetheless, due to these signals’ variability, there is no still a conclusive method of characterization, and it is in fact an open and challenging research problem. In this work, a methodology for comparing features extraction techniques is developed aimed at the discrimination of seismic events of volcanic origin. Representation of the signals in the domain of time, frequency, time-frequency and Cepstral is used. The set of attributes is optimized by selecting characteristics by assigning weights. A supervised classification is executed using known records. Finally, classification performance measures were obtained to determine the subset of characteristics that best represent and discriminate the signals.},
 bibtype = {article},
 author = {Bravo, Yoiner Erazo and Narváez, Edison Rosero and Cabrera, Paola Castro and Bonilla, John Londoño and Ordoñez, Diego Peluffo},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}
}

Currently, researches have been carried out on automatic classification of seismic-volcanic events-mainly based on machine learning techniques-aimed at identifying the nature of the recorded event. In this sense, several approaches have been introduced. Nonetheless, due to these signals’ variability, there is no still a conclusive method of characterization, and it is in fact an open and challenging research problem. In this work, a methodology for comparing features extraction techniques is developed aimed at the discrimination of seismic events of volcanic origin. Representation of the signals in the domain of time, frequency, time-frequency and Cepstral is used. The set of attributes is optimized by selecting characteristics by assigning weights. A supervised classification is executed using known records. Finally, classification performance measures were obtained to determine the subset of characteristics that best represent and discriminate the signals.

@article{
 title = {Data fusion and information quality for biometric identification from multimodal signals},
 type = {article},
 year = {2020},
 keywords = {Biometry,Data fusion,Information quality,Signal processing},
 websites = {https://search.proquest.com/docview/2385757504?pq-origsite=gscholar&fromopenview=true},
 id = {cea5c1ee-4eae-3b71-922a-ef1e63730a97},
 created = {2022-02-02T07:00:22.854Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:22.854Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2020a},
 private_publication = {false},
 abstract = {Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.},
 bibtype = {article},
 author = {Becerra, Miguel A. and Lasso-Arciniegas, Laura and Viveros, Andrés and Serna-Guarín, Leonardo and Peluffo-Ordóñez, Diego and Tobón, Catalina},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}
}

Biometric identification is carried out by processing physiological traits and signals. Biometrics systems are an open field of research and development, since they are permanently susceptible to attacks demanding permanent development to maintain their confidence. The main objective of this study is to analyze the effects of the quality of information on biometric identification and consider it in access control systems. This paper proposes a data fusion model for the development of biometrics systems considering the assessment of information quality. This proposal is based on the JDL (Joint Directors of Laboratories) data fusion model, which includes raw data processing, pattern detection, situation assessment and risk or impact. The results demonstrated the functionality of the proposed model and its potential compared to other traditional identification models.

@article{
 title = {Stochastic-and neuro-fuzzy-analysis-based characterization and classification of 4-channel phonocardiograms for cardiac murmur detection},
 type = {article},
 year = {2020},
 keywords = {ANFIS,Cardiac murmur,Empirical mode decomposition,Hidden markov models,Phonocardiogram},
 websites = {https://search.proquest.com/docview/2451419849/fulltextPDF/F4AF5E590BD14D5EPQ/8},
 id = {6e252017-e3de-3161-9144-bbad74b15fce},
 created = {2022-02-02T07:00:23.247Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:23.247Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2020},
 private_publication = {false},
 abstract = {Cardiac murmurs (CMs) are the most common heart’s diseases that are typically diagnosed from phonocardiogram (PCG) and echocardiogram tests-often supported by computerized systems. Research works have traditionally addressed the automatic CM diagnosis with no distinctively use of the four auscultation areas (one of each cardiac valve), resulting-most probably-in a constrained, nonimpartial diagnostic procedure. This study presents a comparison among four different CM detection systems from a 4-channel PCG. We first evaluate the acoustic characteristics derived from Mel-Frequency Cepstral Coefficients, Empirical Mode Decomposition (EMD), and statistical measures. Secondly, a relevance analysis is carried out using Fuzzy Rough Feature Selection. Thirdly, Hidden Markov Models (HMM), Adaptative Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes, and Gaussian Mixture Model were applied for classification and validated using a 50fold cross-validation procedure with a 70/30 split demonstrating the functionality and capability of EMD, Hidden Markov Model and ANFIS for CM classification.},
 bibtype = {article},
 author = {Becerra, Miguel A. and Delgado-Trejos, Edilson and Mejía-Arboleda, Cristian and Peluffo-Ordóñez, Diego H. and Umaquinga-Criollo, Ana C.},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}
}

Cardiac murmurs (CMs) are the most common heart’s diseases that are typically diagnosed from phonocardiogram (PCG) and echocardiogram tests-often supported by computerized systems. Research works have traditionally addressed the automatic CM diagnosis with no distinctively use of the four auscultation areas (one of each cardiac valve), resulting-most probably-in a constrained, nonimpartial diagnostic procedure. This study presents a comparison among four different CM detection systems from a 4-channel PCG. We first evaluate the acoustic characteristics derived from Mel-Frequency Cepstral Coefficients, Empirical Mode Decomposition (EMD), and statistical measures. Secondly, a relevance analysis is carried out using Fuzzy Rough Feature Selection. Thirdly, Hidden Markov Models (HMM), Adaptative Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes, and Gaussian Mixture Model were applied for classification and validated using a 50fold cross-validation procedure with a 70/30 split demonstrating the functionality and capability of EMD, Hidden Markov Model and ANFIS for CM classification.

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Knee flexion,Motion analysis,Multisensor fusion,Orientation estimation},
 pages = {184-199},
 websites = {http://link.springer.com/10.1007/978-3-030-36636-0_14},
 id = {b0fc8cef-600c-39e3-9888-1676c90a5d8f},
 created = {2022-02-02T07:00:23.555Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:23.555Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Mayorca-Torres2019},
 private_publication = {false},
 abstract = {One way to identify musculoskeletal disorders in the lower limb is through the functional examination where the ranges of normality of the joints are evaluated. Currently, this test can be performed with technological support, with optical sensors and inertial measurement sensors (IMU) being the most used. Kinect has been widely used for the functional evaluation of the human body, however, there are some limits to the movements made in the depth plane and when there is occlusion of the limbs. Inertial measurement sensors (IMU) allow orientation and acceleration measurements to be obtained with a high sampling rate, with some restrictions associated with drift. This article proposes a methodology that combines the acceleration measures of the IMU and kinect sensors in two planes of movement (Frontal and sagittal). These measurements are filtered in the preprocessing stage according to a Kalman filter and are obtained from a mathematical equation that allows them to be merged. The fusion system data obtains acceptable RMS error values of 5.5 and an average consistency of 92.5% for the sagittal plane with respect to the goniometer technique. The data is shown through an interface that allows the visualization of knee joint kinematic data, as well as tools for the analysis of signals by the health professional.},
 bibtype = {inbook},
 author = {Mayorca-Torres, D. and Caicedo-Eraso, Julio C. and Peluffo-Ordoñez, Diego H.},
 doi = {10.1007/978-3-030-36636-0_14},
 chapter = {Method for the Improvement of Knee Angle Accuracy Based on Kinect and IMU: Preliminary Results},
 title = {Communications in Computer and Information Science}
}

One way to identify musculoskeletal disorders in the lower limb is through the functional examination where the ranges of normality of the joints are evaluated. Currently, this test can be performed with technological support, with optical sensors and inertial measurement sensors (IMU) being the most used. Kinect has been widely used for the functional evaluation of the human body, however, there are some limits to the movements made in the depth plane and when there is occlusion of the limbs. Inertial measurement sensors (IMU) allow orientation and acceleration measurements to be obtained with a high sampling rate, with some restrictions associated with drift. This article proposes a methodology that combines the acceleration measures of the IMU and kinect sensors in two planes of movement (Frontal and sagittal). These measurements are filtered in the preprocessing stage according to a Kalman filter and are obtained from a mathematical equation that allows them to be merged. The fusion system data obtains acceptable RMS error values of 5.5 and an average consistency of 92.5% for the sagittal plane with respect to the goniometer technique. The data is shown through an interface that allows the visualization of knee joint kinematic data, as well as tools for the analysis of signals by the health professional.

@inproceedings{
 title = {Recognition of emotions using ICEEMD-based characterization of multimodal physiological signals},
 type = {inproceedings},
 year = {2019},
 pages = {113-116},
 websites = {https://ieeexplore.ieee.org/document/8667585/},
 month = {2},
 publisher = {IEEE},
 id = {f8874b16-9913-33f6-a80d-2f147a52cdda},
 created = {2022-02-02T07:00:23.858Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:23.858Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Ordonez-Bolanos2019},
 private_publication = {false},
 abstract = {Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.},
 bibtype = {inproceedings},
 author = {Ordonez-Bolanos, O. A. and Gomez-Lara, J. F. and Becerra, M. A. and Peluffo-Ordonez, D. H. and Duque-Mejia, C. M. and Medrano-David, D. and Mejia-Arboleda, C.},
 doi = {10.1109/LASCAS.2019.8667585},
 booktitle = {2019 IEEE 10th Latin American Symposium on Circuits & Systems (LASCAS)}
}

Physiological-signal-Analysis-based approaches are typically used for automatic emotion identification. Given the complex nature of signals-related emotions, their right identification often results in a non-Trivial and exhaustive process-especially because such signals suffer from high dependence upon multiple external variables. Some emotional criteria of interest are arousal, valence, and dominance. Several research works have addressed this issue, mainly through creating prediction systems, notwithstanding, due to aspects such as accuracy, in-context interpretation and computational cost, it is still considered a great-of-interest, open research eld. This paper is aimed at verifying the usefulness of the so-called improved complete empirical mode decomposition (ICEEMD) as a physiological-signal-characterization building block within an emotion-predicting system. To this purpose, some physiological signals along with patients' metadata from the DEAP database are considered. The experiments are set-up as follows: Signals are pre-processed by amplitude adjusting and simple filtering. Then, a feature set is built using HC, and multiple statistic measures from information given by the three considered decompositions, namely: ICEEMD, discrete wavelet transform (DWT),and Maximal overlap DWT. Subsequently, Relief F selection algorithm was applied for reducing the dimensionality of the feature space. Finally, classifiers (LDC and K-NN cascade architectures) are used to assess the class-separability given by the feature set. The different decomposition techniques were compared, and the relevant signals and measures were established. Experimental results evidence the suitability of ICEEMD decomposition for physiological-signal-driven emotions analysis.

@article{
 title = {Multi-target tracking for sperm motility measurement using the kalman filter and JPDAF: Preliminary results},
 type = {article},
 year = {2019},
 keywords = {JPDAF,Kalman filter,Morphology,Motility,Spermatozoa},
 websites = {https://search.proquest.com/openview/69fcef4b61d6ec863099124a9c2fe66f},
 id = {5162d12f-c51c-38ec-befe-aae1b353f3b8},
 created = {2022-02-02T07:00:24.162Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:24.162Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Mayorca-Torres2019a},
 private_publication = {false},
 abstract = {The determination of sperm motility characteristics is of great importance for the specification of fertility in men. The semengram is the main diagnostic test to confirm semen quality. Currently, many fertility laboratories use visual assistance techniques to evaluate by using the Makler counting chamber, where motility and sperm count analysis can be performed. This research project proposes a method that allows the quantification of motility through the use of the probabilistic filter (JPDAF) based on the Kalman filter. This research requires the stages of segmentation, feature extraction and development of tracking algorithms for the association of sperm trajectories when there are multiple objectives. A total of 200 individual sperm were selected and the effectiveness for sperm classification was determined according to the mobility categories established by the WHO, obtaining an average value of 93.5% for the categories (A, B, C and D).},
 bibtype = {article},
 author = {Mayorca-Torres, Dagoberto and Guerrero-Chapal, H. and Mejía-Manzano, Julio and Lopez-Mesa, Diana and Peluffo-Ordoñez, Diego H. and Salazar-Castro, José A.},
 journal = {RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao}
}

The determination of sperm motility characteristics is of great importance for the specification of fertility in men. The semengram is the main diagnostic test to confirm semen quality. Currently, many fertility laboratories use visual assistance techniques to evaluate by using the Makler counting chamber, where motility and sperm count analysis can be performed. This research project proposes a method that allows the quantification of motility through the use of the probabilistic filter (JPDAF) based on the Kalman filter. This research requires the stages of segmentation, feature extraction and development of tracking algorithms for the association of sperm trajectories when there are multiple objectives. A total of 200 individual sperm were selected and the effectiveness for sperm classification was determined according to the mobility categories established by the WHO, obtaining an average value of 93.5% for the categories (A, B, C and D).

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Emotion recognition,Improved complementary ensemble empirical mode dec,Multimodal,Physiological signals,Signal processing},
 pages = {351-362},
 websites = {http://link.springer.com/10.1007/978-3-030-14799-0_30},
 id = {d0aff067-cf8f-3b2c-9d1d-44a72d5ae131},
 created = {2022-02-02T07:00:24.460Z},
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 last_modified = {2022-02-02T07:00:24.460Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Gomez-Lara2019},
 private_publication = {false},
 abstract = {The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.},
 bibtype = {inbook},
 author = {Gómez-Lara, J. F. and Ordóñez-Bolaños, O. A. and Becerra, M. A. and Castro-Ospina, A. E. and Mejía-Arboleda, C. and Duque-Mejía, C. and Rodriguez, J. and Revelo-Fuelagán, Javier and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-14799-0_30},
 chapter = {Feature Extraction Analysis for Emotion Recognition from ICEEMD of Multimodal Physiological Signals},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

The emotions identification is a very complex task due to depending on multiple variables individually and as a group. They are evaluated by different criteria such as arousal, valence, and dominance mainly. Several investigations have been focused on building prediction systems. Nevertheless, this is still an open research field. The main objective of this paper is the analysis of the Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD) for feature extraction from physiological signals for emotions prediction. Physiological signals and metadata of the DEAP database were used. First, the signals were preprocessed, then three decompositions were carried out using ICEEMD, Discrete Wavelet Transform (DWT), and Maximal overlap DWT. Feature extraction was carried out using Hermite coefficients, and multiple statistic measures from IMFs, coefficients DWT, and MODWT, and signals. Then, Relief F selection algorithms were applied to reducing the dimensionality of the feature space. Finally, Linear Discriminant Classifier (LDC) and K-NN cascade, and Random Forest classifiers were tested. The different decomposition techniques were compared, and the relevant signals and measures were established. The results demonstrated the capability of ICEEMD decomposition for emotions analysis from physiological signals.

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Electroencephalographic signal,Machine learning,Semantic category,Semantic retrieval,Signal processing},
 pages = {333-342},
 volume = {11466 LNBI},
 websites = {http://link.springer.com/10.1007/978-3-030-17935-9_30},
 publisher = {Springer Verlag},
 id = {2ca92091-083a-354b-9df7-612eee3c9102},
 created = {2022-02-02T07:00:24.746Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:24.746Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Becerra2019},
 private_publication = {false},
 abstract = {Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.},
 bibtype = {inbook},
 author = {Becerra, Miguel Alberto and Londoño-Delgado, Edwin and Botero-Henao, Oscar I. and Marín-Castrillón, Diana and Mejia-Arboleda, Cristian and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-17935-9_30},
 chapter = {Low Resolution Electroencephalographic-Signals-Driven Semantic Retrieval: Preliminary Results},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

Nowadays, there exist high interest in the brain-computer interface (BCI) systems, and there are multiple approaches to developing them. Lexico-semantic (LS) classification from Electroencephalographic (EEG) signals is one of them, which is an open and few explored research field. The LS depends on the creation of the concepts of each person and its context. Therefore, it has not been demonstrated a universal fingerprint of the LS either the spatial location in the brain, which depends on the variability the brain plasticity and other changes throughout the time. In this study, an analysis of LS from EEG signals was carried out. The Emotiv Epoc+ was used for the EEG acquisition from three participants reading 36 different words. The subjects were characterized throughout two surveys (Becks depression, and emotion test) for establishing their emotional state, depression, and anxiety levels. The signals were processed to demonstrate semantic category and for decoding individual words (4 pairs of words were selected for this study). The methodology was executed as follows: first, the signals were pre-processed, decomposed by sub-bands (δ, θ, α, β, and γ ) and standardized. Then, feature extraction was applied using linear and non-linear statistical measures, and the Discrete Wavelet Transform calculated from EEG signals, generating the feature space termed set-1. Also, the principal component analysis was applied to reduce the dimensionality, generating the feature space termed set-2. Finally, both sets were tested independently by multiple classifiers based on the support vector machine and k- nearest neighbor. These were validated using 10-fold cross-validation achieving results upper to 95% of accuracy which demonstrated the capability of the proposed mechanism for decoding LS from a reduced number of EEG signals acquired using a portable system of acquisition.

@inbook{
 type = {inbook},
 year = {2019},
 keywords = {Electroencephalogram (EEG),Epilepsy diagnosis,K-Nearest Neighbors (KNN),Linear Discriminant Analysis (LDA),Quadratic Discriminant Analysis (QDA),Support Vector Machine (SVM)},
 pages = {189-198},
 volume = {11976 LNAI},
 websites = {http://link.springer.com/10.1007/978-3-030-37078-7_19},
 id = {a49b2177-a7de-3ccb-af90-71073fd671a6},
 created = {2022-02-02T07:00:25.028Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {fdde70e8-c730-3a02-9ede-0f48a805037d},
 last_modified = {2022-02-02T07:00:25.028Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {true},
 abstract = {© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.},
 bibtype = {inbook},
 author = {Vega-Gualán, Emil and Vargas, Andrés and Becerra, Miguel and Umaquinga, Ana and Riascos, Jaime A. and Peluffo, Diego},
 doi = {10.1007/978-3-030-37078-7_19},
 chapter = {Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

© 2019, Springer Nature Switzerland AG. Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The framework uses a well-known epilepsy dataset and runs several experiments for two and three classification problems. The results suggest that DWT decomposition with SVM is the most suitable combination.

@inproceedings{
 title = {Cardiac Murmur Effects on Automatic Segmentation of ECG Signals for Biometric Identification: Preliminary Study},
 type = {inproceedings},
 year = {2019},
 keywords = {Automatic segmentation,Biometric,Electrocardiographic signal,Heart murmur,Pattern recognition},
 pages = {269-279},
 websites = {https://link.springer.com/chapter/10.1007/978-3-030-14799-0_23,http://link.springer.com/10.1007/978-3-030-14799-0_23},
 id = {f0963143-c77e-38ed-902f-169b406fe4b1},
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 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
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 starred = {false},
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 confirmed = {true},
 hidden = {false},
 citation_key = {Duque-Mejia2019},
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 abstract = {Biometric identification or authentication is a pattern recognition process, which is carried out acquiring different measures of human beings to distinguish them. Fingerprint and eye iris are the most known and used biometric techniques; nevertheless, also they are the most vulnerable to counterfeiting. Consequently, nowadays research has been focused on physiological signals and behavioral traits for biometric identification because these allow not only the authentication but also determine that the subject is alive. Electrocardiographic signals (ECG-S) have been studied for biometric identification demonstrating their capability. Taking into account that some pathologies are detected using ECG-S, these can affect the results of biometric identification; nonetheless, some diseases such as cardiac murmurs are not detected by ECG-S, but they can distort their morphology. Therefore, these signals must be analyzed considering different pathologies. In this paper, a biometric study was carried out from 40 subjects (20 with cardiac murmurs and 20 without cardiac affections). First, the ECG-S were preprocessed and segmented using the fast method for detecting T waves with annotation of P and T waves, then feature extraction was carried out using discrete wavelet transform (DWT), maximal overlap DWT, cepstral coefficients, and statistical measures. Then, rough set and relief F algorithms were applied to datasets (pathological and normal signals) for attribute reduction. Finally, multiple classifiers and combinations of them were tested. The results of the segmentation were analyzed achieving low results for signals affected by cardiac murmurs. On the other hand, according to the cardiac murmur effects analyzed, the performance of the classifiers in cascade shown the best accuracy for human identification from ECG-S, minimizing the impact of variability generated on ECG-S by cardiac murmurs diseases.},
 bibtype = {inproceedings},
 author = {Duque-Mejía, C. and Becerra, M. A. and Zapata-Hernández, C. and Mejia-Arboleda, C. and Castro-Ospina, A. E. and Delgado-Trejos, E. and Peluffo-Ordóñez, Diego H. and Rosero-Montalvo, P. and Revelo-Fuelagán, Javier},
 doi = {10.1007/978-3-030-14799-0_23},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

Biometric identification or authentication is a pattern recognition process, which is carried out acquiring different measures of human beings to distinguish them. Fingerprint and eye iris are the most known and used biometric techniques; nevertheless, also they are the most vulnerable to counterfeiting. Consequently, nowadays research has been focused on physiological signals and behavioral traits for biometric identification because these allow not only the authentication but also determine that the subject is alive. Electrocardiographic signals (ECG-S) have been studied for biometric identification demonstrating their capability. Taking into account that some pathologies are detected using ECG-S, these can affect the results of biometric identification; nonetheless, some diseases such as cardiac murmurs are not detected by ECG-S, but they can distort their morphology. Therefore, these signals must be analyzed considering different pathologies. In this paper, a biometric study was carried out from 40 subjects (20 with cardiac murmurs and 20 without cardiac affections). First, the ECG-S were preprocessed and segmented using the fast method for detecting T waves with annotation of P and T waves, then feature extraction was carried out using discrete wavelet transform (DWT), maximal overlap DWT, cepstral coefficients, and statistical measures. Then, rough set and relief F algorithms were applied to datasets (pathological and normal signals) for attribute reduction. Finally, multiple classifiers and combinations of them were tested. The results of the segmentation were analyzed achieving low results for signals affected by cardiac murmurs. On the other hand, according to the cardiac murmur effects analyzed, the performance of the classifiers in cascade shown the best accuracy for human identification from ECG-S, minimizing the impact of variability generated on ECG-S by cardiac murmurs diseases.

@inproceedings{
 title = {Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results},
 type = {inproceedings},
 year = {2019},
 keywords = {Emotion recognition,Physiological signals,Signal processing},
 pages = {363-373},
 volume = {11466 LNBI},
 websites = {https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33},
 publisher = {Springer Verlag},
 id = {eb0e3d93-f7e7-3302-b648-f25a4e8cf159},
 created = {2022-02-02T07:00:25.713Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
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 last_modified = {2022-02-02T07:00:25.713Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.},
 bibtype = {inproceedings},
 author = {Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-17935-9_33},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Classification,Computer-aided diagnosis,Optimization,Voice pathology},
 pages = {148-159},
 websites = {http://link.springer.com/10.1007/978-3-030-00350-0_13},
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 citation_key = {Areiza-Laverde2018},
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 abstract = {Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, medical diagnosis procedures in different scenarios. Particularly, in the context of voice pathology detection, the use of machine learning algorithms has proved to be a promising and suitable alternative. This work proposes the implementation of two well known classification algorithms, namely artificial neural networks (ANN) and support vector machines (SVM), optimized by particle swarm optimization (PSO) algorithm, aimed at classifying voice signals between healthy and pathologic ones. Three different configurations of the Saarbrucken voice database (SVD) are used. The effect of using balanced and unbalanced versions of this dataset is proved as well as the usefulness of the considered optimization algorithm to improve the final performance outcomes. Also, proposed approach is comparable with state-of-the-art methods.},
 bibtype = {inbook},
 author = {Areiza-Laverde, Henry Jhoán and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-00350-0_13},
 chapter = {Voice Pathology Detection Using Artificial Neural Networks and Support Vector Machines Powered by a Multicriteria Optimization Algorithm},
 title = {Communications in Computer and Information Science}
}

Computer-aided diagnosis (CAD) systems have allowed to enhance the performance of conventional, medical diagnosis procedures in different scenarios. Particularly, in the context of voice pathology detection, the use of machine learning algorithms has proved to be a promising and suitable alternative. This work proposes the implementation of two well known classification algorithms, namely artificial neural networks (ANN) and support vector machines (SVM), optimized by particle swarm optimization (PSO) algorithm, aimed at classifying voice signals between healthy and pathologic ones. Three different configurations of the Saarbrucken voice database (SVD) are used. The effect of using balanced and unbalanced versions of this dataset is proved as well as the usefulness of the considered optimization algorithm to improve the final performance outcomes. Also, proposed approach is comparable with state-of-the-art methods.

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Data fusion,Diagnostic decision support,Multimodal fusion,Physiological signal,Signal processing},
 pages = {1-15},
 websites = {http://link.springer.com/10.1007/978-3-319-98998-3_1},
 id = {e21f10d1-f52d-34eb-ae8b-62a9beef23f7},
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 read = {false},
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 abstract = {The analysis of physiological signals is widely used for the development of diagnosis support tools in medicine, and it is currently an open research field. The use of multiple signals or physiological measures as a whole has been carried out using data fusion techniques commonly known as multimodal fusion, which has demonstrated its ability to improve the accuracy of diagnostic care systems. This paper presents a review of state of the art, putting in relief the main techniques, challenges, gaps, advantages, disadvantages, and practical considerations of data fusion applied to the analysis of physiological signals oriented to diagnosis decision support. Also, physiological signals data fusion architecture oriented to diagnosis is proposed.},
 bibtype = {inbook},
 author = {Uribe, Y. F. and Alvarez-Uribe, K. C. and Peluffo-Ordoñez, D. H. and Becerra, M. A.},
 doi = {10.1007/978-3-319-98998-3_1},
 chapter = {Physiological Signals Fusion Oriented to Diagnosis - A Review},
 title = {Communications in Computer and Information Science}
}

The analysis of physiological signals is widely used for the development of diagnosis support tools in medicine, and it is currently an open research field. The use of multiple signals or physiological measures as a whole has been carried out using data fusion techniques commonly known as multimodal fusion, which has demonstrated its ability to improve the accuracy of diagnostic care systems. This paper presents a review of state of the art, putting in relief the main techniques, challenges, gaps, advantages, disadvantages, and practical considerations of data fusion applied to the analysis of physiological signals oriented to diagnosis decision support. Also, physiological signals data fusion architecture oriented to diagnosis is proposed.

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {ANN,EMG signals,Feature extraction,KNN,Parzen},
 pages = {368-375},
 websites = {http://link.springer.com/10.1007/978-3-030-01132-1_42},
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 abstract = {The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.},
 bibtype = {inbook},
 author = {Lasso-Arciniegas, Laura and Viveros-Melo, Andres and Salazar-Castro, José A. and Becerra, Miguel A. and Castro-Ospina, Andrés Eduardo and Revelo-Fuelagán, E. Javier and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-01132-1_42},
 chapter = {Movement Identification in EMG Signals Using Machine Learning: A Comparative Study},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

The analysis of electromyographic (EMG) signals enables the development of important technologies for industry and medical environments, due mainly to the design of EMG-based human-computer interfaces. There exists a wide range of applications encompassing: Wireless-computer controlling, rehabilitation, wheelchair guiding, and among others. The semantic interpretation of EMG analysis is typically conducted by machine learning algorithms, and mainly involves stages for signal characterization and classification. This work presents a methodology for comparing a set of state-of-the-art approaches of EMG signal characterization and classification within a movement identification framework. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification performance of (90.89 ± 1.12)% (KNN), (93.92 ± 0.34)% (ANN) and 91.09 ± 0.93 (Parzen-density-based classifier) with 12 movements.

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Classification,EMG signals,Movements selection,Wavelet},
 pages = {139-149},
 websites = {http://link.springer.com/10.1007/978-3-319-98998-3_11},
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 abstract = {Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).},
 bibtype = {inbook},
 author = {Viveros-Melo, A. and Lasso-Arciniegas, L. and Salazar-Castro, J. A. and Peluffo-Ordóñez, D. H. and Becerra, M. A. and Castro-Ospina, A. E. and Revelo-Fuelagán, E. J.},
 doi = {10.1007/978-3-319-98998-3_11},
 chapter = {Exploration of Characterization and Classification Techniques for Movement Identification from EMG Signals: Preliminary Results},
 title = {Communications in Computer and Information Science}
}

Today, human-computer interfaces are increasingly more often used and become necessary for human daily activities. Among some remarkable applications, we find: Wireless-computer controlling through hand movement, wheelchair directing/guiding with finger motions, and rehabilitation. Such applications are possible from the analysis of electromyographic (EMG) signals. Despite some research works have addressed this issue, the movement classification through EMG signals is still an open challenging issue to the scientific community -especially, because the controller performance depends not only on classifier but other aspects, namely: used features, movements to be classified, the considered feature-selection methods, and collected data. In this work, we propose an exploratory work on the characterization and classification techniques to identifying movements through EMG signals. We compare the performance of three classifiers (KNN, Parzen-density-based classifier and ANN) using spectral (Wavelets) and time-domain-based (statistical and morphological descriptors) features. Also, a methodology for movement selection is proposed. Results are comparable with those reported in literature, reaching classification errors of 5.18% (KNN), 14.7407% (ANN) and 5.17% (Parzen-density-based classifier).

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Electroencephalographic signal,Sensorial stimulus,Signal processing,Tactile pleasantness},
 pages = {309-316},
 websites = {http://link.springer.com/10.1007/978-3-030-01132-1_35},
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 citation_key = {Becerra2018b},
 private_publication = {false},
 abstract = {Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.},
 bibtype = {inbook},
 author = {Becerra, Miguel A. and Londoño-Delgado, Edwin and Pelaez-Becerra, Sonia M. and Castro-Ospina, Andrés Eduardo and Mejia-Arboleda, Cristian and Durango, Julián and Peluffo-Ordóñez, Diego H.},
 doi = {10.1007/978-3-030-01132-1_35},
 chapter = {Electroencephalographic Signals and Emotional States for Tactile Pleasantness Classification},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

Haptic textures are alterations of any surface that are perceived and identified using the sense of touch, and such perception affects individuals. Therefore, it has high interest in different applications such as multimedia, medicine, marketing, systems based on human-computer interface among others. Some studies have been carried out using electroencephalographic signals; nevertheless, this can be considered few. Therefore this is an open research field. In this study, an analysis of tactile stimuli and emotion effects was performed from EEG signals to identify pleasantness and unpleasantness sensations using classifier systems. The EEG signals were acquired using Emotiv Epoc+ of 14 channels following a protocol for presenting ten different tactile stimuli two times. Besides, three surveys (Becks depression, emotion test, and tactile stimuli pleasant level) were applied to three volunteers for establishing their emotional state, depression, anxiety and the pleasantness level to characterize each subject. Then, the results of the surveys were computed and the signals preprocessed. Besides, the registers were labeled as pleasant and unpleasant. Feature extraction was applied from Short Time Fourier Transform and discrete wavelet transform calculated to each sub-bands (ƍ, θ, α, β, and γ) of EEG signals. Then, Rough Set algorithm was applied to identify the most relevant features. Also, this technique was employed to establish relations among stimuli and emotional states. Finally, five classifiers based on the support vector machine were tested using 10-fold cross-validation achieving results upper to 99% of accuracy. Also, dependences among emotions and pleasant and unpleasant tactile stimuli were identified.

@inbook{
 type = {inbook},
 year = {2018},
 keywords = {Biometric identification,Cardiac murmur,Electrocardiographic signal,Signal processing},
 pages = {410-418},
 websites = {http://link.springer.com/10.1007/978-3-030-03493-1_43},
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 abstract = {The process of distinguishing among human beings through the inspection of acquired data from physical or behavioral traits is known as biometric identification. Mostly, fingerprint- and iris-based biometric techniques are used. Nowadays, since such techniques are highly susceptible to be counterfeited, new biometric alternatives are explored mainly based on physiological signals and behavioral traits -which are useful not only for biometric identification purposes, but may also play a role as a vital signal indicator. In this connection, the electrocardiographic (ECG) signals have shown to be a suitable approach. Nonetheless, their informative components (morphology, rhythm, polarization, and among others) can be affected by the presence of a cardiac pathology. Even more, some other cardiac diseases cannot directly be detected by the ECG signal inspection but still have an effect on their waveform, that is the case of cardiac murmurs. Therefore, for biometric purposes, such signals should be analyzed submitted to the effects of pathologies. This paper presents a exploratory study aimed at assessing the influence of the presence of a pathology when analyzing ECG signals for implementing a biometric system. For experiments, a data base holding 20 healthy subjects and 20 pathological subjects (diagnosed with different types of cardiac murmurs) are considered. The proposed signal analysis consists of preprocessing, characterization (using wavelet features), feature selection and classification (five classifiers as well as a mixture of them are tested). As a result, through the performed comparison of the classification rates when testing pathological and normal ECG signals, the cardiac murmurs’ undesired effect on the identification mechanism performance is clearly unveiled.},
 bibtype = {inbook},
 author = {Becerra, M. A. and Duque-Mejía, C. and Zapata-Hernández, C. and Peluffo-Ordóñez, D. H. and Serna-Guarín, L. and Delgado-Trejos, Edilson and Revelo-Fuelagán, E. J. and Blanco Valencia, X. P.},
 doi = {10.1007/978-3-030-03493-1_43},
 chapter = {Exploratory Study of the Effects of Cardiac Murmurs on Electrocardiographic-Signal-Based Biometric Systems},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

The process of distinguishing among human beings through the inspection of acquired data from physical or behavioral traits is known as biometric identification. Mostly, fingerprint- and iris-based biometric techniques are used. Nowadays, since such techniques are highly susceptible to be counterfeited, new biometric alternatives are explored mainly based on physiological signals and behavioral traits -which are useful not only for biometric identification purposes, but may also play a role as a vital signal indicator. In this connection, the electrocardiographic (ECG) signals have shown to be a suitable approach. Nonetheless, their informative components (morphology, rhythm, polarization, and among others) can be affected by the presence of a cardiac pathology. Even more, some other cardiac diseases cannot directly be detected by the ECG signal inspection but still have an effect on their waveform, that is the case of cardiac murmurs. Therefore, for biometric purposes, such signals should be analyzed submitted to the effects of pathologies. This paper presents a exploratory study aimed at assessing the influence of the presence of a pathology when analyzing ECG signals for implementing a biometric system. For experiments, a data base holding 20 healthy subjects and 20 pathological subjects (diagnosed with different types of cardiac murmurs) are considered. The proposed signal analysis consists of preprocessing, characterization (using wavelet features), feature selection and classification (five classifiers as well as a mixture of them are tested). As a result, through the performed comparison of the classification rates when testing pathological and normal ECG signals, the cardiac murmurs’ undesired effect on the identification mechanism performance is clearly unveiled.

@inbook{
 type = {inbook},
 year = {2018},
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 last_modified = {2022-02-02T07:00:27.989Z},
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 citation_key = {Becerra2018a},
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 abstract = {Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.},
 bibtype = {inbook},
 author = {Becerra, M. A. and Londoño-Delgado, E. and Pelaez-Becerra, S. M. and Serna-Guarín, L. and Castro-Ospina, A. E. and Marin-Castrillón, D. and Peluffo-Ordóñez, D. H.},
 doi = {10.1007/978-3-319-98998-3_10},
 chapter = {Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States},
 title = {Communications in Computer and Information Science}
}

Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands δ, θ, α, β, γ of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands β and α. Finally, relations among emotion, EEG, and odors were demonstrated.