Scalable NPairLoss-Based Deep-ECG for ECG Verification

Abstract

In recent years, Electrocardiogram (ECG) applications are blooming, such as cardiovascular disease detection and mental condition assessment. To protect the sensitive ECG data from data breach, ECG biometrics system are proposed. Compared to the traditional biometric systems, ECG biometric is known to be ubiquitous, difficult to counterfeit and more suitable in cleanroom or IC fabs. ECG biometric system mainly contains identification task and verification task, and Deep-ECG is the state-of-the-art work in both tasks. However, Deep-ECG only trained on one specific dataset, which ignored the intra-variability of different ECG signals across different situations. Moreover, Deep-ECG used cross-entropy loss to train the deep convolutional neural networks (CNN) model, which is not the most appropriate loss function for such embedding-based problem. In this paper, to solve the above problems, we proposed a scalable NPairLoss-based Deep-ECG (SNL-Deep-ECG) system for ECG verification on a hybrid dataset, mixed with four public ECG datasets. We modify the preprocessing method and trained the deep CNN model with NPairLoss. Compared with Deep-ECG, SNL-Deep-ECG can reduce 90% of the signal collection time during inference with only 0.9% AUC dropped. Moreover, SNL-Deep-ECG outperforms Deep-ECG for approximately 3.5% Area Under ROC Curve (AUC) score in the hybrid dataset. Moreover, SNL-Deep-ECG can maintain its verification performance over the increasing number of the subjects, and thus to be scalable in terms of subject number. The final performance of the proposed SNL-Deep-ECG is 0.975/0.970 AUC score on the seen/unseen-subject task.