The major function of heart is to pump blood to tissues and organs necessary for the body metabolism. It is therefore one of the organs that affects human life. However, adverse situations, such as paralysis and death are the major problems that can lead to a heart failure. Healthy heart is very important to live comfortably. To prevent adverse events, it is important to monitor and detect heart diseases early. The aim of proposed method is to determine and classify nine types of ECG arrhythmias, including normal beats. A large feature set was obtained from the MIT-BIH Arrhythmia database. Zhao Atlas-Mark time-frequency distribution was used to extract the feature set. Five classification algorithms have been tried. The Cubic Support Vector Machine algorithm yielded best performance results. The proposed method achieved accuracy, sensitivity, specificity, F-score, positive predictive, and negative predictive values of 96.39%, 94.22%, 92.02%, 93.91%, 93.90% and 96.72%, respectively. Considering the data size, performance values, and number of arrythmias, the proposed method provided superiority to other studies. Furthermore, running time is suitable for telemedicine systems.

心脏的主要功能是将血液泵送到身体新陈代谢所必需的组织和器官。因此，它是影响人类生活的器官之一。但是，诸如麻痹和死亡之类的不利情况是可能导致心力衰竭的主要问题。健康的心脏对于舒适地生活非常重要。为了预防不良事件，重要的是及早监测和发现心脏病。拟议方法的目的是确定和分类9种类型的ECG心律失常，包括正常心跳。从MIT-BIH心律失常数据库中获得了较大的功能集。使用赵阿特拉斯-马克时间-频率分布提取特征集。已经尝试了五种分类算法。三次支持向量机算法产生了最佳性能结果。所提出的方法实现了准确性，灵敏度 特异性，F评分，阳性预测值和阴性预测值分别为96.39％，94.22％，92.02％，93.91％，93.90％和96.72％。考虑到数据大小，性能值和心律失常的数量，该方法提供了优于其他研究的优势。此外，运行时间适用于远程医疗系统。