ECG Based Biometric Identification for Population with Normal and Cardiac Anomalies Using Hybrid HRV and DWT Features

Electrocardiograms (ECG) emerged as a novel biometric identification system in the past decade which yields high level of uniqueness and permanence. Moreover ECG provides inherent characteristic of liveness of a person, so it can furnish a superior solution as compared to other biometric techniques. This research provides with the complete systematic approach for ECG based person identification in various cardiac conditions and consists of ECG preprocessing, feature extraction, feature reduction and classifier performance. Segmentation of ECG involve R-peak detection, however system is independent of fiducial detection and does not require any extensive computational complexity. Feature extraction involve fusion of discrete wavelet transform (DWT) of cardiac cycle and heart rate variability (HRV) based features. Feature reduction is performed with best first search and classification is performed by using Random Forests. System is tested on three publicly available databases like MIT-BIH/Arrhythmia (MITDB), MIT-BIH/Normal Sinus Rhythm (NSRDB) and ECGID database (ECG-IDDB) including all subjects. HRV effects are removed from MITDB to confront with cardiac disorders that cause problems in identification and accuracy of 95.85% was achieved with false acceptance rate (FAR) of 4.15% and false rejection rate (FRR) of 0.1%. System is also tested on normal population based databases and accuracy of 100% is achieved using NSRDB database and 83.88% for a challenging ECG-ID database.