心跳種類的分析與判斷：模糊C-平均值演算法

Abstract

This study proposes a simple and reliable method, termed the Fuzzy C-Means method (FCMM) for classifying the heartbeat cases from electrocardiogram (ECG) signals. The FCMM can accurately classify and distinguish the difference between normal heartbeats and abnormal heartbeats. Classifying the heartbeat cases from ECG signals consists of four main procedures: (i) QRS extraction stage for detecting QRS waveform using the Difference Operation Method; (ii) qualitative features stage for qualitative feature selection on ECG signals; (iii) Procedure-FCM is used to compute the cluster center for each class; and (iv) Procedure-HCD is used to determine the heartbeat case for the patient. The available ECG records in the MIT-BIH arrhythmia database are utilized to illustrate the effectiveness of the proposed method. Experimental results show that the total classification accuracy was approximately 93.78%.