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A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data

The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented,...

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Autores principales: Carrillo-Alarcón, Juan Carlos, Morales-Rosales, Luis Alberto, Rodríguez-Rángel, Héctor, Lobato-Báez, Mariana, Muñoz, Antonio, Algredo-Badillo, Ignacio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308921/
https://www.ncbi.nlm.nih.gov/pubmed/32498271
http://dx.doi.org/10.3390/s20113139
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author Carrillo-Alarcón, Juan Carlos
Morales-Rosales, Luis Alberto
Rodríguez-Rángel, Héctor
Lobato-Báez, Mariana
Muñoz, Antonio
Algredo-Badillo, Ignacio
author_facet Carrillo-Alarcón, Juan Carlos
Morales-Rosales, Luis Alberto
Rodríguez-Rángel, Héctor
Lobato-Báez, Mariana
Muñoz, Antonio
Algredo-Badillo, Ignacio
author_sort Carrillo-Alarcón, Juan Carlos
collection PubMed
description The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.
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spelling pubmed-73089212020-06-25 A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data Carrillo-Alarcón, Juan Carlos Morales-Rosales, Luis Alberto Rodríguez-Rángel, Héctor Lobato-Báez, Mariana Muñoz, Antonio Algredo-Badillo, Ignacio Sensors (Basel) Article The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data. MDPI 2020-06-02 /pmc/articles/PMC7308921/ /pubmed/32498271 http://dx.doi.org/10.3390/s20113139 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Carrillo-Alarcón, Juan Carlos
Morales-Rosales, Luis Alberto
Rodríguez-Rángel, Héctor
Lobato-Báez, Mariana
Muñoz, Antonio
Algredo-Badillo, Ignacio
A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title_full A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title_fullStr A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title_full_unstemmed A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title_short A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
title_sort metaheuristic optimization approach for parameter estimation in arrhythmia classification from unbalanced data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7308921/
https://www.ncbi.nlm.nih.gov/pubmed/32498271
http://dx.doi.org/10.3390/s20113139
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