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.Comment: 44 pages, 9 figure