An Analysis of ECG for Determining Heartbeat Case by Using the Principal Component Analysis and Fuzzy Logic
This study proposes a Principal Component Analysis (PCA) and Fuzzy Logic to analyze ECG signals for effective determining heartbeat case. It 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), atrial premature contractions (APC), and paced beat (PB). Analysis of the ECG signals consists of three major stages: (1) detecting the QRS waveform; (2) the qualitative features selection; and (3) heartbeat case determination. This study uses Principal Component Analysis for selection of qualitative features, and determination of heartbeat case is carried out by fuzzy logic. Records of MIT-BIH database are used for performance evaluation. In the experiments, the sensitivities were 97.74%, 91.54%, 93.53%, 90.29%, 89.78% and 84.25% for heartbeat cases NORM, LBBB, RBBB, VPC, APC and PB, respectively. The total classification accuracy (TCA) is about 94.03%.
fuzzy logic ecg signals mit-bih database