Sensors, Vol. 20, Pages 3139: A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
Sensors, Vol. 20, Pages 3139: A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data
Sensors doi: 10.3390/s20113139
Authors:
Juan Carlos Carrillo-Alarcón
Luis Alberto Morales-Rosales
Héctor Rodríguez-Rángel
Mariana Lobato-Báez
Antonio Muñoz
Ignacio Algredo-Badillo
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 c...
Source: Sensors - Category: Biotechnology Authors: Juan Carlos Carrillo-Alarc ón Luis Alberto Morales-Rosales H éctor Rodríguez-Rángel Mariana Lobato-B áez Antonio Mu ñoz Ignacio Algredo-Badillo Tags: Article Source Type: research
More News: Arrhythmia | Biotechnology | Cardiology | Databases & Libraries | Electrocardiogram | Heart