Cardiac arrhythmia diagnosis Method using Fuzzy C-Means algorithm on ECG signals

In this study, “Fuzzy C-Means Method (FCMM)” is applied for classifying the cardiac arrhythmia on ECG signals, the FCMM consists of three main stages: (i) QRS extraction stage for detecting QRS waveform using the Difference Operation Method; (ii) qualitative features stage for qualitative feature selection using the Range-Overlaps Method on ECG signals; (iii) Fuzzy C-Means algorithm is used to determine the cardiac arrhythmia for the patient. The FCMM can accurately classify the normal heartbeats (NORM) and abnormal heartbeats. Abnormal heartbeats include Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Ventricular Premature Contractions (VPC) and Atrial Premature Contractions (APC). 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%.