ECG-Based Biometric Authentication System Using Hybrid Signal Matching

Biometric authentication systems are increasingly used to improve security in modern digital environments. Traditional authentication mechanisms such as passwords and PIN codes suffer from duplication and credential leakage. Electrocardiogram signals represent the electrical activity of the human heart and provide a unique physiological biometric. This work proposes an ECG-based biometric authentication system using an AD8232 sensor and Arduino UNO. The ECG signal is sampled at 250 Hz and transmitted to a Python backend for signal processing. A hybrid authentication model combining Dynamic Time Warping distance, RR interval comparison, and amplitude difference analysis is implemented. Experimental testing shows that ECG signals can distinguish individuals under controlled conditions.