Analyzing ECG for cardiac arrhythmia using cluster analysis

Abstract

This work proposes a method of analyzing ECG signal to diagnose cardiac arrhythmias utilizing the cluster analysis (CA) method. The proposed method can accurately classify and distinguish the difference between normal heartbeats (NORM) and abnormal heartbeats. Abnormal heartbeats may include the following: left bundle branch block (LBBB), right bundle branch block (RBBB), ventricular premature contractions (VPC), and atrial premature contractions (APC). Analysis of ECG signal consists of three major stages: (i) detecting the QRS waveform; (ii) selecting qualitative features; and (iii) determining heartbeat case. The ECG signals in the MIT-BIH arrhythmia database are adopted as reference data for accomplishing the first two stages, and cluster analysis is used to determine patient heartbeat case. In the experiments, the sensitivity is 95.59%, 91.32%, 90.50%, 94.51%, and 93.77% for heartbeat case NORM, LBBB, RBBB, VPC, and APC, respectively. The total classification accuracy (TCA) was about 94.30%.