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: Tags: Article Source Type: research