ECG biometric analysis in cardiac irregularity conditions

Biometric traits offer direct solutions to the critical security concerns involved in identity authentication systems. In this paper, a systematic analysis of the electrocardiogram (ECG) signal for application in human recognition is reported, suggesting that cardiac electrical activity is highly personalized in a population. Features extracted from the autocorrelation of healthy ECG signals embed considerable diacritical power, and render fiducial detection unnecessary. The central consideration of this paper is the evaluation of an identification system that is robust to common cardiac irregularities such as premature ventricular contraction (PVC) and atrial premature contraction (APC). Criteria concerning the power distribution and complexity of ECG signals are defined to bring to light abnormal ECG recordings, which are not employable for identification. Experimental results indicate a recognition rate of 96.2% and render identification based on ECG signals rather promising.

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