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A cluster-based opposition differential evolution algorithm boosted by a local search for ECG signal classification
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Electrocardiogram (ECG) signals, which capturethe heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.
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Funding Information:
This work is funded by FCT/MCTES through national funds and when applicable co-funded EU Funds under the project UIDB/EEA/50008/2020. We express our sincere gratitude to the Fundação para a Ciência e a Tecnologia (FCT), Portugal, for the generous grant of computational resources that have been pivotal in conducting our research. This project, identified by reference 2023.10386.CPCA.A0 (https://doi.or g/10.54499/2023.10386.CPCA.A0), was made possible through the National Advanced Computing Network (RNCA), under the operational center HPCUÉ_Oblivion. Furthermore, we would like to express our sincere gratitude for the support and funding received from multiple sources. Firstly, we acknowledge the Centro Regional Operational Program (Centro 2020) for their contribution within the scope of research activities of Project CENTRO-01–0145-FEDER-000019-C4-Cloud Computing Competence Center. Secondly, we appreciate the funding from the Pilots for Healthy and Active Ageing (Pharaon) project of the European Union’s Horizon 2020 research and innovation programme under the grant agreement no. 857188. This work is supported by Fundação para a Ciência e Tecnologia UIDB/00645/2020 (https://doi.org/10.54499/UIDB/00645/2020). This work was supported by FCT-Fundação para a Ciência e Tecnologia, I.P. by project reference UIDB/50008/2020, and DOI identifier https://doi.org/10.54499/UIDB/50008/2020. This research was funded by the Swedish Knowledge Foundation through the Research Profile NIIT.
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© 2025 The Authors
Palavras-chave
Clustering Differential evolution ECG analysis Neural networks Opposition-based learning Regularization Theoretical Computer Science General Computer Science Modelling and Simulation