Abstract
Human identification from the biological signal the Electrocardiogram (ECG) has been demonstrated in several studies. In this paper, we present a new technique for personal identification using short time Fourier transform (STFT) and histograms of four fiducial QRS features. We examined the applicability of our methodology on 162 ECG records of 81 subjects from the publicly available ECG ID data base. Our experiments show that the normalized Euclidean STFT distance can identify individuals with 95 % accuracy. Hence, with fusing six histogram distances computed from the QRS fiducial features and applying majority voting, the identification accuracy increases up to 100 %. These findings indicate that ECG is sufficiently unique signal and can be useful as biometric identifier.