Small Scale Single Pulse ECG-based Authentication using GLRT that Considers T Wave Shift and Adaptive Template Update with Prior Information

Abstract

Electrocardiogram (ECG) has been investigated as a promising biometric for the last two decades by exploiting the difference of ECG signals between people. However, it is still challenging to take ECG signal variation of one person into account. ECG of one person may vary due to person’s multiple states (e.g., tension, relax, cardio exercise) or anatomical / physiological changes of one’s heart over a long period of time (e.g., heart disease). It has been shown that these types of ECG signal variations resulted in low authentication task performance. We propose a generalized likelihood ratio test (GLRT) based authentication metric that considers T wave shift. Our proposed GLRT based method does not require to know heart rate (HR) that can not be usually obtained when using single pulse ECG. We also propose an adaptive ECG template update scheme based on penalized maximum likelihood estimator with prior information, previously obtained ECG template. Our proposed methods do not require high computation complexity and other people’s ECG information so that they can be potentially implemented in small scale devices such as low cost wearable bands with limited access to others’ ECG data. Proposed methods were evaluated with the public ECG-ID database (89 subjects) from the PhysioNet that contains varying HR and acquisitions over multiple days for some subjects. ECG Set S denotes partial data of ECG-ID that contains 2 records per subject that were collected on different sessions of the same day and ECG Set A denotes another data set including ECG Set S and additional 2 records per subject for 25 subjects that were collected on different sessions partially on different days. A classical Euclidean metric yielded 4.7% EER (equal error rate) for ECG Set S and 8.1% EER for ECG Set A. Our proposed GLRT based metric yielded improved EER over Euclidean distance: 3.9% for ECG Set S and 6.5% for ECG Set A. Proposed GLRT metric with adaptive template update achieved 4.8% EER for ECG Set A.