COMBINING FORWARD-BACKWARD RR INTERVAL DETECTOR WITH ARRHYTHMIC BEAT CLASSIFICATION IN ECG SIGNALS

An Electrocardiogram (ECG) provides valuable information about the functional aspects of the heart and cardiovascular system. This research develops a Forward-Backward (FB) algorithm to extract the QRS complex and combines with the arrhythmic beat classification rules on medical knowledge. The purpose of this study is to increase the R-wave detection accuracy and to monitor a patient’s arrhythmia condition in real time. The FB algorithm augments forward and backward searching rules from QRS onset and eliminates lower amplitude signal near the baseline using statistical process control (SPC) concept to improve the R-wave detection performance. The MIT-BIH arrhythmia database (MITDB) is used as training data to determine the parameters on proposed approach and classified into four arrhythmia beat types, i.e., normal (N), premature ventricular contraction (PVC), ventricular flutter/fibrillation (VF) and second-degree heart block (BII) beat. According to QRS complex extraction and beat classification rules, the proposed method provides 99.52 % accuracy in the detection of R-wave location; 99.14 % for N classified beats; 93.37 % for PVC classified beats; 93.00 % for VF classified beats and 89.852 % for BII classified beats in the MITDB. To conclude, the proposed procedure could be useful because it only uses RR interval for arrhythmic beat classification and is affected slightly by noisy and complicated signal