Physiology-based augmented deep neural network frameworks for ECG biometrics with short ECG pulses considering varying heart rates

摘要

Electrocardiogram (ECG) has been investigated as promising biometrics with high authentication accuracy, natural liveness test ability, and wearable sensor availability. There have been many algorithms developed for ECG biometric authentication or identification including recent state-of-the-art deep learning (DL) methods that usually yielded excellent performance with real ECG data in ideal conditions. However, one of the challenges against ideal conditions is the intra-personal variability of ECG pulses due to heart beat rate changes. Due to this variability, ECG based biometric methods have experienced significant performance degradation. It is especially challenging when a small number of ECG pulses must be used for biometrics with fast response authentication since there is not enough information available to correct for different heart rates. In this letter, we investigated DL based ECG biometrics with the input of a small number of ECG pulses considering varying heart rates. We propose physiology-based augmented deep neural network (DNN) frameworks for ECG biometric methods that are based on the Hodges' QT interval correction. Unlike QT interval correction methods, our proposed framework does not require the estimated heart rate. Our proposed training and testing schemes were evaluated with representative DL based biometric methods using CNN and RNN with very short ECG pulses (1 or 3 pulses per authentication) from the public multi-session ECG-ID dataset (83 subjects). We exploited the ECG-ID dataset to simulate the challenging scenario including the enrollment and authentication happening over relatively long time duration so that heart rate variation is likely occurring. Our augmented DNN frameworks yielded significantly better performance than the original DL based biometrics; up to 11.7% improvement in accuracy and 8.6% improvement in sensitivity simultaneously with 99.9% specificity.(c) 2022 Elsevier B.V. All rights reserved.