Achieving stability of ECG biometric features through binaural brain entrainment

In this paper, it is shown that classification of features from heart (electrocardiogram, ECG) signals for biometric purposes (i.e. for individual identification) degrades over a period of time and a method based on binaural brain entrainment is proposed to minimise the variations in the heart signals over time to improve the classification performance. The results indicate that variability of the heart features is reduced by 15.57% using the proposed method and this results in improving the classification accuracy from 90.35% to 95.77% when tested with five subjects with ECG data recorded over a period of six months. This pilot study indicates that binaural brain entrainment can be used to improve the stability of ECG features over time thereby increasing its potential to be used in biometric applications.