2023

Nath, Rajdeep K; Tervonen, Jaakko; Närväinen, Johanna; Pettersson, Kati; Mäntyjärvi, Jani

This paper presents a novel approach of an ECG-based mental health biometric system that relies on ultra-short duration (2 seconds) of one-channel ECG signal segments from acute stress data for accurate user identification and authentication. The proposed method uses a simple framework for contrastive learning (SimCLR) to train the user identification and authentication models. The performance of the proposed ECG-based biometric system was evaluated for a single-session use case using an in-house dataset. The dataset consisted of ECG signals acquired during a study protocol designed to induce physical and mental stress. The proposed biometric system was able to achieve an accuracy of 98% for user identification and an equal error rate (EER) of 0.02 when trained and tested with a balanced condition with stress and baseline/recovery. Our proposed system was able to retain its accuracy to 95% and the EER to 0.05 even when the training size was significantly reduced.