Electrocardiogram (ECG) signals are recordings of the heart's electrical activity and are widely used in the medical field to diagnose various cardiac conditions and monitor heart function. The accurate classification of ECG signals 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. To this end, after the preprocessing step, we extracted several features such as BPM, IBI, and SDNN. Then, the features are fed into a 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. Population-based metaheuristic techniques have been effectively used to address this. 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）信号是记录心脏电活动的信号，广泛应用于医疗领域诊断各种心脏疾病及监测心脏功能。准确分类ECG信号对于心脏相关疾病的早期检测与治疗至关重要。本文提出一种基于改进差分进化（DE）算法的新方法用于ECG信号分类。为此，在预处理步骤后，我们提取了BPM、IBI和SDNN等特征，随后将这些特征输入多层感知机（MLP）。尽管MLP仍广泛用于ECG信号分类，但使用基于梯度的训练方法（此过程最常用的算法）存在显著缺陷，例如可能陷入局部最优解。基于种群的元启发式技术已被有效用于解决此问题。本文采用一种增强型DE算法作为最有效的种群算法之一进行训练。为此，我们基于聚类策略、对立学习和局部搜索改进了DE算法：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。改进DE算法寻得的权重和偏置随后被输入六种基于梯度的局部搜索算法。换言之，DE算法获得的权重被用作初始化点。因此，我们引入了六种不同局部搜索算法对应的训练方法。通过大量实验，我们证明提出的训练算法能比传统训练算法取得更优结果。