Rhythm-adaptive signal processing for effective ECG and PPG-based authentication under dynamic physiological conditions.

This study presents a rhythm-adaptive framework for electrocardiogram (ECG)- and photoplethysmogram (PPG)-based biometric authentication under mild physical activity, where heart-rate variability and cycle-to-cycle morphological changes reduce the reliability of stationary cardiac representations. The approach models ECG and PPG signals as cyclic random processes (CRPs) with variable rhythm and uses the estimated rhythm function to align cardiac cycles before extracting compact statistical features. The evaluation was performed on the walking records of the public PhysioNet Pulse Transit Time PPG Dataset (PTTPD), version 1.1.0, using all 22 subjects and the synchronized ECG and pleth_1 channels. The protocol is within-session because PTTPD provides one recording session per subject/activity; therefore, the highest ECG values are interpreted as strong within-session separability rather than as evidence of long-term biometric permanence. In the final classification tables, rhythm-adaptive averages of three cardiac cycles were used for ECG and seven cardiac cycles for PPG, following the optimization curves. For ECG, the best hybrid classifiers reached Accuracy, Balanced Accuracy andF1-score of 1.000, with FRR = 0.000, FAR = 0.000 and HTER = 0.000 for Linear support vector machine. For PPG, the best configuration reached Accuracy = 0.962, Balanced Accuracy = 0.961 andF1-score = 0.960 with FRR = 0.057, FAR = 0.020 and HTER = 0.039 for Nearest Neighbors. These results show that CRP-based rhythm-adaptive processing stabilizes cardiac-cycle morphology during movement and provides an interpretable alternative to purely data-driven representations for wearable and telemedicine authentication systems.