Effects of diseased ECG on the robustness of ECG biometric systems

Abstract

This paper looks into the effects of diseased subjects on the recognition rate of an ECG biometric system. A novel technique for feature extraction, linear predictive coding, is implemented along with neural networks for the classifier. Diseased ECG has been shown reduce the recognition rate of the system by only less than 1% and thus the system is robust towards diseased ECG. This allows for the system incorporating linear predictive coding to be used in practical situations where some users may not be aware of their health state and may have diseased ECG signals.