Cardiac arrhythmia disorder is known as one of the most common diseases in the world. Today, this disease is considered as the leading cause of death in industrial and semi-industrial societies. Various tools and methods have been developed to study the detection of heart diseases, based on analyzing the electrocardiogram (ECG) signal. Due to the simplicity and noninvasive nature, ECG signals are vastly used by physicians to determine the heart problems and abnormalities.

In this paper, a computer-aided diagnosis (CAD) system is provided for the automated classification and accurate diagnosis of seven types of cardiac arrhythmias using the ECG signal. The basis of this method is using machine learning algorithms to classify normal rhythm and six abnormal cardiac functions. In the proposed method, after the pre-processing stage, the ECG signal is segmented, and various morphological characteristics, frequency domain features, and nonlinear indices are extracted for the ECG signal. Several metaheuristic optimization algorithms are used to remove redundant or irrelevant features and reduce the feature space dimension. These are used on the combination of the extracted features in which, non-dominated sorting genetic algorithm (NSGA II) as a multi-objective optimization algorithm has the best performance. Furthermore, various machine learning algorithms include k-nearest neighbor (KNN), feed-forward neural network (FF net), fitting neural network (Fit net), radial basis function neural network (RBFNN) and pattern recognition network (Pat net) are employed for the classification. The highest accuracy obtained based on ten-fold cross-validation from the FF net is 98.75%, demonstrates the efficiency of the proposed method and the achieved improvement compared to the other similar works with the same dataset. The combination of a vast and various features from morphology, frequency, and nonlinear characteristics to demonstrate the diverse aspects of ECG signals as well as employing a multi-objective meta-heuristic optimization algorithm for selecting the more correlated features are the main contributions of this study.

心律失常被称为世界上最常见的疾病之一。如今，这种疾病被认为是工业和半工业社会的主要死亡原因。在分析心电图（ECG）信号的基础上，已开发出各种工具和方法来研究心脏病的检测。由于其简单性和无创性，医生广泛使用ECG信号来确定心脏问题和异常情况。

本文提供了一种计算机辅助诊断（CAD）系统，用于使用ECG信号对7种类型的心律不齐进行自动分类和准确诊断。该方法的基础是使用机器学习算法对正常心律和六个异常心脏功能进行分类。在提出的方法中，在预处理阶段之后，对ECG信号进行分段，并提取ECG信号的各种形态特征，频域特征和非线性指标。几种元启发式优化算法用于删除多余或不相关的特征并减小特征空间尺寸。这些用于提取特征的组合，其中非支配排序遗传算法（NSGA II）作为多目标优化算法具有最佳性能。此外，各种机器学习算法包括k近邻（KNN），前馈神经网络（FF net），拟合神经网络（Fit net），径向基函数神经网络（RBFNN）和模式识别网络（Pat net）分类。基于十倍交叉验证从FF网络获得的最高准确度是98.75％，证明了与相同数据集的其他类似作品相比，该方法的效率和所实现的改进。结合形态学，频率和非线性特征等多种特征来证明ECG信号的各个方面，并采用多目标元启发式优化算法来选择更相关的特征是本研究的主要贡献。