A novel fuzzy c-means method for classifying heartbeat cases from ECG signals

This study proposes a simple and reliable method termed the fuzzy c-means method for classifying the heartbeat cases from electrocardiogram (ECG) signals. The proposed method has the advantages of good detection results, no complex mathematic computations, low memory space and low time complexity. 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) Procedure-DOM for detecting QRS waveform using the Difference Operation Method; (ii) qualitative features stage (Procedure-ROM) for qualitative feature selection using the Range-Overlaps Method on ECG signals; (iii) Procedure-CCC 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 experiments show that the sensitivities were 98.28%, 90.35%, 86.97%, 92.19%, and 94.86% for NORM, LBBB, RBBB, VPC and APC, respectively. The total classification accuracy was approximately 93.57%.