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.

翻译：心电图（ECG）信号记录心脏电活动，用于诊断和监测心脏问题。准确分类ECG信号（尤其是区分不同类型心律失常和心肌梗死）对心脏相关疾病的早期发现与治疗至关重要。本文提出一种基于改进差分进化（DE）算法的新型ECG信号分类方法以提升性能。在方法初始阶段，预处理步骤后从ECG信号中提取若干重要特征，这些特征随后作为增强型多层感知器（MLP）的输入。尽管MLP仍广泛用于ECG信号分类，但基于梯度的训练方法（训练过程中最常用的算法）存在显著缺陷，例如易陷入局部最优。本文采用改进的差分进化（DE）算法作为训练过程的核心——该算法是目前最有效的群体智能算法之一。为此，我们基于聚类策略、对立学习和局部搜索对DE进行改进。聚类策略可充当交叉算子，而对立算子的目标是提升DE算法的探索能力。将改进DE算法优化得到的权重与偏置输入六个梯度型局部搜索算法，即DE所得权重作为初始化起点。由此，我们引入六种不同训练算法（对应不同局部搜索算法）。通过大量实验证明，本文提出的训练算法能获得优于传统训练方法的结果。