Investigation of human identification using two-lead Electrocardiogram (ECG) signals

In this paper, we investigate the applicability of Electrocardiogram (ECG) signals for human identification. Wavelet Transform (WT) and Independent Component Analysis (ICA) methods are applied to extract morphological features that appear to offer excellent discrimination among subjects. The proposed method is aimed at the two-lead ECG configuration that is routinely used in long-term continuous monitoring of heart activity. The information from the two ECG leads is fused to achieve improved subject identification. The proposed method was tested on three public ECG databases, namely, MIT-BIH Arrhythmias Database, Ml T-BIH Normal Sinus Rhythm Database and Long-Term ST Database, in order to evaluate the proposed subject identification method on normal ECG signals as well as ECG signals with arrhythmias. Excellent rank-1 recognition rates (as high as 99.6%) were achieved based on single heartbeats. The proposed method exhibits good identification accuracies not just with the normal ECG signals, but also in the presence of various arrhythmias. This work adds to the growing evidence that ECG signals can be useful for human identification.