Statistical Analysis for Human Authentication Using ECG Waves

Automated security is one of the major concerns of modern times. Secure and reliable authentication of a person is in great demand. A biometric trait like the electrocardiogram (ECG) of a person is unique and secure. In this paper we propose an authentication system based on ECG by using statistical features like mean and variance of ECG waves. Statistical tests like Z−test, t−test and χ2−tests are used for checking the authenticity of an individual. Then confusion matrix is generated to find False Acceptance Ratio (FAR) and False Rejection Ratio (FRR). This methodology of authentication is tested on data set of 200 waves prepared from ECG samples of 40 individuals taken from Physionet QT Database. The proposed authentication system is found to have FAR of about 2.56% and FRR of about 0.13%. The overall accuracy of the system is found to be 99.81%.