A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

• URL: http://arxiv.org/abs/2305.02731v2
• Date: Fri, 6 Oct 2023 16:03:47 GMT
• Title: A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification
• Authors: Mehran Pourvahab, Seyed Jalaleddin Mousavirad, Virginie Felizardo, Nuno Pombo, Henriques Zacarias, Hamzeh Mohammadigheymasi, Sebasti\~ao Pais, Seyed Nooreddin Jafari, Nuno M.Garcia
• Abstract summary: This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification. In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.
• Score: 1.9738259547092656
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Electrocardiogram (ECG) signals, which capture the 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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