Members | SDAS - Smart Data Analysis Systems Group

Email: miguel.becerra@sdas-group.com

He is an electronic engineer specialized in the analysis and processing of biomedical signals. The main goal of his research is implementing biometric systems and decision support tools to help clinicians in pathology comprehension, diagnosis, and patients' treatment.

Research Interests

Signal processing, data fusion, machine learning.

Publications

Low Data Fusion Framework Oriented to Information Quality for BCI Systems
International Conference on Bioinformatics and Biomedical Engineering 2018
Miguel Alberto Becerra, Karla C. Alvarez-Uribe, Diego Hernán Peluffo Ordoñez.
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Abstract

The evaluation of the data/information fusion systems does not have standard quality criteria making the reuse and optimization of these systems a complex task. In this work, we propose a complete low data fusion (DF) framework based on the Joint Director of Laboratories (JDL) model, which considers contextual information alongside information quality (IQ) and performance evaluation system to optimize the DF process according to the user requirements. A set of IQ criteria was proposed by level. The model was tested with a brain-computer interface (BCI) system multi-environment to prove its functionality. The first level makes the selection and preprocessing of electroencephalographic signals. In level one feature extraction is carried out using discrete wavelet transform (DWT), nonlinear and linear statistical measures, and Fuzzy Rough Set – FRS algorithm for selecting the relevant features; finally, in the same level a classification process was conducted using support vector machine – SVM. A Fuzzy Inference system is used for controlling different processes based on the results given by an IQ evaluation system, which applies quality measures that can be weighted by the users of the system according to their requirements. Besides, the system is optimized based on the results given by the cuckoo search algorithm, which uses the IQ traceability for maximizing the IQ criteria according to user requirements. The test was carried out with different type and levels of noise applied to the signals. The results showed the capability and functionality of the model.
Advances in Homotopy Applied to Object Deformation
International Conference on Bioinformatics and Biomedical Engineering
Jose Alejandro Salazar-Castro, Ana Cristina Umaquinga-Criollo, Lilian Dayana Cruz-Cruz, Luis Omar Alpala-Alpala, Catalina González-Castaño, Miguel A Becerra-Botero, Diego Hernán Peluffo-Ordóñez, Cesar Germán Castellanos-Domínguez
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Abstract

This work explores novel alternatives to conventional linear homotopy to enhance the quality of resulting transitions from object deformation applications. Studied/introduced approaches extend the linear mapping to other representations that provides smooth transitions when deforming objects while homotopy conditions are fulfilled. Such homotopy approaches are based on transcendental functions (TFH) in both simple and parametric versions. As well, we propose a variant of an existing quality indicator based on the ratio between the coefficients curve of resultant homotopy and that of a less-realistic, reference homotopy. Experimental results depict the effect of proposed TFH approaches regarding its usability and benefit for interpolating images formed by homotopic objects with smooth changes.
Case-Based Reasoning Systems for Medical Applications with Improved Adaptation and Recovery Stages
International Conference on Bioinformatics and Biomedical Engineering
X Blanco Valencia, D Bastidas Torres, C Piñeros Rodriguez, Diego Hernán Peluffo-Ordóñez, Miguel A Becerra, Andrés Eduardo Castro-Ospina
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Abstract

Case-Based Reasoning Systems (CBR) are in constant evolution, as a result, this article proposes improving the retrieve and adaption stages through a different approach. A series of experiments were made, divided in three sections: a proper pre-processing technique, a cascade classification, and a probability estimation procedure. Every stage offers an improvement, a better data representation, a more efficient classification, and a more precise probability estimation provided by a Support Vector Machine (SVM) estimator regarding more common approaches. Concluding, more complex techniques for classification and probability estimation are possible, improving CBR systems performance due to lower classification error in general cases.
Developments on Solutions of the Normalized-Cut-Clustering Problem Without Eigenvectors
International Symposium on Neural Networks
Leandro Leonardo Lorente-Leyva, Israel David Herrera-Granda, Paul D Rosero-Montalvo, Karina L Ponce-Guevara, Andrés Eduardo Castro-Ospina, Miguel A Becerra, Diego Hernán Peluffo-Ordóñez, José Luis Rodríguez-Sotelo
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Abstract

Normalized-cut clustering (NCC) is a benchmark graph-based approach for unsupervised data analysis. Since its traditional formulation is a quadratic form subject to orthogonality conditions, it is often solved within an eigenvector-based framework. Nonetheless, in some cases the calculation of eigenvectors is prohibitive or unfeasible due to the involved computational cost – for instance, when dealing with high dimensional data. In this work, we present an overview of recent developments on approaches to solve the NCC problem with no requiring the calculation of eigenvectors. Particularly, heuristic-search and quadratic-formulation-based approaches are studied. Such approaches are elegantly deduced and explained, as well as simple ways to implement them are provided.
Data fusion from multiple stations for estimation of PM2. 5 in specific geographical location
Iberoamerican Congress on Pattern Recognition
Miguel A Becerra, Marcela Bedoya Sánchez, Jacobo García Carvajal, Jaime A Guzmán Luna, Diego H Peluffo-Ordóñez, Catalina Tobón
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Abstract

Nowadays, an important decrease in the quality of the air has been observed, due to the presence of contamination levels that can change the natural composition of the air. This fact represents a problem not only for the environment, but also for the public health. Consequently, this paper presents a comparison among approaches based on Adaptive Neural Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) for the estimation level of PM2.5 (Particle Material 2.5) in specific geographic locations based on nearby stations. The systems were validated using an environmental database that belongs to air quality network of Valle de Aburrá (AMVA) of Medellin Colombia, which has the registration of 5 meteorological variables and 2 pollutants that are from 3 nearby measurement stations. Therefore, this project analyses the relevance of the characteristics obtained in every single station to estimate the levels of PM2.5 in the target station, using four different selectors based on Rough Set Feature Selection (RSFS) algorithms. Additionally, five systems to estimate the PM2.5 were compared: three based on ANFIS, and two based on SVR to obtain an aim and an efficient mechanism to estimate the levels of PM2.5 in specific geographic locations fusing data obtained from the near monitoring stations.
Kernel-based framework for spectral dimensionality reduction and clustering formulation: A theoretical study
ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal
Xiomara Patricia Blanco Valencia, MA Becerra, AE Castro Ospina, M Ortega Adarme, D Viveros Melo, DH PELUFFO ORDÓÑEZ
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Abstract

This work outlines a unified formulation to represent spectral approaches for both dimensionality reduction and clustering. Proposed formulation starts with a generic latent variable model in terms of the projected input data matrix. Particularly, such a projection maps data onto a unknown high-dimensional space. Regarding this model, a generalized optimization problem is stated using quadratic formulations and a least-squares support vector machine. The solution of the optimization is addressed through a primal-dual scheme. Once latent variables and parameters are determined, the resultant model outputs a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Particularly, proposed formulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.
Analysis of motor imaginary BCI within multi-environment scenarios using a mixture of classifiers
Colombian Conference on Computing
M Ortega-Adarme, M Moreno-Revelo, DH Peluffo-Ordoñez, D Marín Castrillon, AE Castro-Ospina, MA Becerra
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Abstract

Brain-computer interface (BCI) is a system that provides communication between human beings and machines through an analysis of human brain neural activity. Several studies on BCI systems have been carried out in controlled environments, however, a functional BCI should be able to achieve an adequate performance in real environments. This paper presents a comparative study on alternative classification options to analyze motor imaginary BCI within multi-environment real scenarios based on mixtures of classifiers. The proposed methodology is as follows: The imaginary movement detection is carried out by means of feature extraction and classification, in the first stage; feature set is obtained from wavelet transform, empirical mode decomposition, entropy, variance and rates between minimum and maximum, in the second stage, where several classifier combinations are applied. The system is validated using a database, which was constructed using the Emotiv Epoc+ with 14 channels of electroencephalography (EEG) signals. These were acquired from three subject in 3 different environments with the presence and absence of disturbances. According to the different effects of the disturbances analyzed in the three environments, the performance of the mixture of classifiers presented better results when compared to the individual classifiers, making it possible to provide guidelines for choosing the appropriate classification algorithm to incorporate into a BCI system.
Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach
Interpreting Cardiac Electrograms-From Skin to Endocardium
Catalina Tobón, Andrés Orozco‐Duque, Juan P Ugarte, Miguel Becerra, Javier Saiz
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Abstract

Identi cation of atrial brillation (AF) mechanisms could improve the rate of ablation success. However, the incomplete understanding of those mechanisms makes di cult the decision of targeting sites for ablation. This work is focused on the importance of EGM analysis for detecting and modulating rotors to guide ablation procedures and improve its outcomes. Virtual atrial models are used to show how nonlinear measures can be used to generate electroanatomical maps to detect critical sites in AF. A description of the atrial cell mathematical models, and the procedure of coupling them within two‐dimensional and three‐dimensional virtual atrial models in order to simulate arrhythmogenic mecha‐ nisms, is given. Mathematical modeling of unipolar and bipolar electrogramas (EGM) is introduced. It follows a discussion of EGM signal processing. Nonlinear descriptors, such as approximate entropy and multifractal analysis, are used to study the dynamical behavior of EGM signals, which are not well described by a linear law. Our results evince that nonlinear analysis of EGM can provide information about the dynamics of rotors and other mechanisms of AF. Furthermore, these brillatory pa erns can be simulated using virtual models. The combination of features using machine learning tools can be used for identifying arrhythmogenic sources of AF.
Data Fusion Applied to Biometric Identification–A Review
Colombian Conference on Computing
JC Zapata, CM Duque, Y Rojas-Idarraga, ME Gonzalez, JA Guzmán, MA Becerra Botero
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Abstract

There is a growing interest in data fusion oriented to identification and authentication from biometric traits and physiological signals, because of its capacity for combining multiple sources and multimodal analysis allows improving the performance of these systems. Thus, we considered necessary make an analytical review on this domain. This paper summarizes the state of the art of the data fusion oriented to biometric authentication and identification, exploring its techniques, benefits, advantages, disadvantages, and challenges.
Emociones cromáticas: análisis de la percepción de color basado en emociones y su relación con el consumo de moda
Anagramas-Rumbos y sentidos de la comunicación-
Sonia M Peláez Becerra, Paula Gómez Gómez, Miguel A Becerra
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Abstract

The globalization of the marketplace, requires that products are innovative, commercial, functional and highly striking, to wake up as well in the consumer, the sufficient attention and achieve that you give the time of the purchase, with prior identification of your need and the acceptance of the product. Characterize the consumer is complex and different aspects like color and emotions must be analyzed. In this work is established, the relationship between emotion, color and fashion object, identifying variables that motivate the consumer at the time of your purchase. So a survey was applied on a university population in Colombia and Additionally, through database belonging DEAP 4 universities in Europe (Queen Mary University of London, University of Twente, Netherlands, University of Geneva and at the EPFL in Switzerland), that related emotions and videos in a university population of Europe, made an analysis of the color in the background of these and showed the influence of the color using the graph of Genève. The results obtained showed, a strong influence of the color in the attitude of consumption and the color on the emotions, although it was not possible to conclude that emotions affect the time of purchase by the consumer.
On the relationship between dimensionality reduction and spectral clustering from a kernel viewpoint
Distributed Computing and Artificial Intelligence, 13th International Conference
Diego Hernán Peluffo-Ordóñez, Miguel A Becerra, Andrés Eduardo Castro-Ospina, X Blanco-Valencia, Juan C Alvarado-Pérez, Roberto Therón, A Anaya-Isaza
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Abstract

This paper presents the development of a unified view of spectral clustering and unsupervised dimensionality reduction approaches within a generalized kernel framework. To do so, the authors propose a multipurpose latent variable model in terms of a high-dimensional representation of the input data matrix, which is incorporated into a least-squares support vector machine to yield a generalized optimization problem. After solving it via a primal-dual procedure, the final model results in a versatile projected matrix able to represent data in a low-dimensional space, as well as to provide information about clusters. Specifically, our formulation yields solutions for kernel spectral clustering and weighted-kernel principal component analysis.
Effect of the electrograms density in detecting and ablating the tip of the rotor during chronic atrial fibrillation: an in silico study
European Society of Cardiology
Juan P Ugarte, Catalina Tobón, Andrés Orozco-Duque, Miguel A Becerra, John Bustamante
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Abstract

Identification in situ of arrhythmogenic mechanisms could improve the rate of ablation success in atrial fibrillation (AF). Our research group reported that rotors could be located through dynamic approximate entropy (DApEn) maps. However, it is unknown how much the spatial resolution of catheter electrodes could affect substrates localization. The present work looked for assessing the electrograms (EGMs) spatial resolution needed to locate the rotor tip using DApEn maps.
Heart murmur detection using Ensemble Empirical Mode Decomposition and derivations of the Mel-Frequency Cepstral Coefficients on 4-area phonocardiographic signals
Computing in Cardiology Conference (CinC), 2014
Joe A Jimenez, Miguel A Becerra, Edilson Delgado-Trejos
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Abstract

This paper presents an automatic detection system for the classification of phonocardiographic (PCG) signals using 4 standard auscultation areas (one of each cardiac valve) for heart murmur diagnosis. The database of 4-area PCG records belongs to the National University of Colombia. A set of 50 individuals were labeled as normal, while 98 were labeled as exhibiting cardiac murmurs, caused by valve disorders. With the help of medical experts, 400 representative beats were chosen, 200 normal and 200 with evidence of cardiac murmur from 4 different areas of auscultation. First, the PCG signals were preprocessed; next, four different derivations of Mel Frequency Cepstral Coefficients (MFCC) were extracted. Additionally, statistical moments of Hilbert Huang Transform (HHT) were estimated using different combinations of the signal components by means of Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD with Adaptative Noise (CEEMDAN), independently, where the computational complexity were compared. Finally, stochastic analysis of the feature space was carried out by an ergodic-HMM and the global classification result was around 98% with acceptable sensitivity and specificity scores, using a 30-fold cross-validation procedure (70/30 split).
Análisis de dependencia usando regresión lineal múltiple y regresión de componentes principales en contaminantes delaire y parámetros meteorológicos caso de estudio
Revista QUID
Daniel Parra, Silvia Quijano, Diana Orrego, Miguel Becerra
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Abstract

La contaminación del aire ha sido un problema ambiental y de salud en el mundo, se han propuesto varios métodos de regresión lineal multivariable para determinar niveles de asociación y dependencia entre contaminantes del aire y parámetros meteorológicos. Estos parámetros se caracterizan por tener información redundante e irrelevante, que es perjudicial para un modelo de regresión. En este contexto, un análisis de la dependencia mediante Regresión Lineal Múltiple y Regresión de Componentes Principales de los contaminantes del aire y parámetros meteorológicos fue desarrollado, encontrando una fuerte correlación entre el ozono y la temperatura (0.8) para la estación Tlalnepantla, así como un coeficiente de determinación de 82%. El modelo de PCR demostró que parámetros meteorológicos como temperatura y humedad relativa tienen un impacto significativo en la contaminación del aire en la Ciudad de México.
Simple ablation guided by ApEn mapping in a 2D model during permanent atrial fibrillation
Computing in Cardiology Conference (CinC), 2014
Catalina Tobón, Laura C Palacio, Juan E Duque, Esteban A Cardona, Juan P Ugarte, Andrés Orozco-Duque, Miguel A Becerra, Javier Saiz, John Bustamante
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Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia. Catheter ablation has become the main therapeutic strategy for the treatment of paroxysmal AF, however, results in permanent AF are not completely satisfactory; complex ablation patterns are needed, thus it is of great interest to look for an ideal pattern with a minimum number of lines. Ablation of complex fractionated atrial electrograms (CFAE) has been proposed for the termination of rotors as mechanisms of AF maintenance. The aim of this work is to characterize the CFAE by implementing approximate entropy (ApEn) and to correlate it with the tip of a simulated rotor, in order to apply simple ablation patterns to terminate the rotor activity. For this, a rotor was simulated in a 2D model of human atrial tissue, under permanent AF conditions. Electrograms were recorded and an algorithm to measure ApEn was developed. Three ablation patterns were proposed and evaluated. The ApEn allowed locating the CFAE with high precision and relating them to the tip of the rotor. The ablation pattern that enclosed the rotor tip and that was extended to a conduction boundary was effective in the termination of the rotor. The ApEn could be very effective in identifying target sites for ablation.
Reconstruction of Multi Spatial Resolution Feature Maps on a 2D Model of Atrial Fibrillation: Simulation Study
VI Latin American Congress on Biomedical Engineering CLAIB 2014, Paraná, Argentina 29, 30 & 31 October 2014
Juan Murillo-Escobar, Miguel A Becerra, Esteban A Cardona, Catalina Tobón, Laura C Palacio, BE Valdés, Diana A Orrego
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Abstract

Catheter ablation is a technique used as treatment for atrial fibrillation, this procedure is guided using 3D electro anatomic mapping systems. Ablation is one of the treatments for AF whose effectiveness depend on the location of the rotor tip and this depend of the quality of mapping obtained from a re-duced set of real signals. This paper presents a comparison study between three approaches for reconstruction of features maps of a 2D model of simulated atrial fibrillation. The model was char-acterized using the mean, Shannon entropy and approximate entropy of the electrograms (EGM). The model is made up of 22500 EGM and reductions of 75%, 93.5% and 97.3% in the spatial resolution of the model was conducted. Thereupon a reconstruction of the feature maps was realized using inverse distance weighted (IDW), inverse distance weighted-median filter (IDW-MF) and backpropagation artificial neural networks (BPANN), the performance of the techniques was analyzed using the root mean square error (RMSE) and the peak signal to noise ratio (PSNR). IDW shows a general RSME of 4.2% and a PSNR of 27.5dB, IDW-MF exhibited a RSME of 17.5% and a PSNR of 21.8 dB, finally BPANN shows a RSME of 9% and PSNR of 22.8 dB. IDW shows the best performance at any degree of reduction while IDW-MF represent the best approach for Shannon entropy and mean maps reconstruction and IDW has the best perfor-mance for approximate entropy maps reconstruction.
Interfaz cerebro computador basado en señales EEG para el control de movimiento de una prótesis de mano usando ANFIS.
Lámpsakos
Alexandra Bedoya Rojas, Jessica Giraldo Leiva, Ingrid Durley Torres Pardo, Miguel A Becerra
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Abstract

A large number of people in the world who have amputated limbs that are usually replaced by mechanical prostheses. Moreover, electromechanical prostheses have been gaining strength and are supported by different types of interfaces as brain interfaces computer that improve the functionality of these, despite show representative results for the control of prostheses, it is still an open field research that seeks to improve its effectiveness and efficiency. In this paper, we present a methodology for classification of electroencephalographic signals (EEG) to control movement of a prosthetic hand, based on the adaptive neurofuzzy inference system (ANFIS) applied to features derived from the wavelet transform is presented (TW ) and the rough fuzzy sets (FRS) to EEG signals obtained in 10-10 system. Thus the performance of the proposed system was measured using cross-validation with 30 repetitions 70-30 obtaining high performance in terms of accuracy, which means that this model has potential as a classifier for the detection of EEG changes for the command generation for the control of hand movement.
Adaptive neuro-fuzzy inference system for acoustic analysis of 4-channel phonocardiograms using empirical mode decomposition
Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE
Miguel A Becerra, Diana A Orrego, Edilson Delgado-Trejos
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Abstract

The heart's mechanical activity can be appraised by auscultation recordings, taken from the 4-Standard Auscultation Areas (4-SAA), one for each cardiac valve, as there are invisible murmurs when a single area is examined. This paper presents an effective approach for cardiac murmur detection based on adaptive neuro-fuzzy inference systems (ANFIS) over acoustic representations derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of 4-channel phonocardiograms (4-PCG). The 4-PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated on the combination of different intrinsic mode functions (IMFs). A fuzzy-rough feature selection (FRFS) was applied in order to reduce complexity. An ANFIS network was implemented on the feature space, randomly initialized, adjusted using heuristic rules and trained using a hybrid learning algorithm made up by least squares and gradient descent. Global classification for 4-SAA was around 98.9% with satisfactory sensitivity and specificity, using a 50-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-PCG and the neuro-fuzzy inference of acoustic features offered a high performance to detect cardiac murmurs.
Stochastic analysis and classification of 4-area cardiac auscultation signals using Empirical Mode Decomposition and acoustic features
Computing in Cardiology (CinC)
MA Becerra, DA Orrego, Carolina Mejia, Edilson Delgado-Trejos
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Abstract

As cardiac murmurs do not generally appear in every area of auscultation, this paper presents an effective approach for cardiac murmur detection based on stochastic analysis of acoustic features derived from Empirical Mode Decomposition (EMD) and Hilbert-Huang Transform (HHT) of phonocardiographic (PCG) signals made up by the 4-Standard Auscultation Areas (SAA). The 4-SAA PCG database belongs to the National University of Colombia. Mel-Frequency Cepstral Coefficients (MFCC) and statistical moments of HHT were estimated over EMD components. An ergodic HMM was applied on the feature space, randomly initialized and trained by expectation maximization with a convergence at 10e-6 and a maximum iteration number of 1000. Global classification results for 4-SAA were around 98.7% with satisfactory sensitivity and specificity results, using a 30-fold cross-validation procedure (70/30 split). The representation capability of the EMD technique applied to 4-SAA PCG signals and stochastic analysis of acoustic features offered a high performance to detect cardiac murmurs.
Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Diana A Orrego, Miguel A Becerra, Edilson Delgado-Trejos
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Abstract

This paper presents a dimensionality reduction study based on fuzzy rough sets with the aim of increasing the discriminant capability of the representation of normal ECG beats and those that contain ischemic events. A novel procedure is proposed to obtain the fuzzy equivalence classes based on entropy and neighborhood techniques and a modification of the Quick Reduct Algorithm is used to select the relevant features from a large feature space by a dependency function. The tests were carried out on a feature space made up by 840 wavelet features extracted from 900 ECG normal beats and 900 ECG beats with evidence of ischemia. Results of around 99% classification accuracy are obtained. This methodology provides a reduced feature space with low complexity and high representation capability. Additionally, the discriminant strength of entropy in terms of representing ischemic disorders from time-frequency information in ECG signals is highlighted.
Análisis comparativo de tecnologías de transmisión
Revista QUID
Miguel A Becerra Botero, Oscar A Mejía Gonzales, Darío A Murillo, Sandra M Álvarez
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Abstract

Los sistemas de control de parquet) desempeñan una función clave en el marco de los sistemas inteligentes de transportes, La disponibilidad de las celdas de parqueo es difícil de controlar, por lo que se requiere un sistema de monitoreo que envié at sistema de administración de manera permanente, la disponibilidad de cada una de las celdas y permita una administración del parqueadero más eficiente. Las microondas terrestres son utilizadas en la transmisión inalámbrica de señales, caracterizándose por su bajo consumo de potencia y direccionalidad siendo ideales para la transmisión, en rango reducido, de señales de control basadas en protocolos coma Bluetooth, ZigBee y Wi-Fi. El presente artículo realiza un análisis comparativo de estas tecnologías, con el fin de seleccionar la más apropiada para ser aplicada en la red de sensores requerida para el sistema do control de celdas del parqueadero.

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