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ECG based biometric human identification using chaotic encryption

Biometric identification provides a reliable performance on recognition of identity for different security and authentication management problems. With the development of technology, the privacy and security is the main concern as technology can be falsified. In recent decades biometric traits are the trusted choice to the researchers and there are strong documentations on biometric traits like human electrocardiogram which is the time changing electrical impulse signal passing through heart is able to use for identity subject identification. In this work, an analytic and serviceable analysis of the ECG for human identification is presented. A new approach based on chaotic encryption (CE) of continuous wavelet transformed (CWT) ECG signal has been proposed. The CWT used here to obtain similarity pattern of each individual and CE of the CWT decomposed signal used for subject identification. The proposed approach has been tested using 11 different subjects ECG signal available at MIT-BIH arrhythmia database. The obtained results show that the identification rate is 96.82%. The findings of this study can be helpful to develop effective biometric human identification system.