A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Person identification with arrhythmic ECG signals using deep convolution neural network

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person's beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

ECG Identity Recognition Based on Feature Reuse Residual Network

With the increasing demand for security and privacy, identity recognition based on the unique biometric features of ECG signals is gaining more and more attention. This paper proposes a feature reuse residual network (FRRNet) model to address the problem that the recognition accuracy of conventional ECG identification methods decreases with the increase in the number of testing samples at different moments or in different heartbeat cycles. The residual module of the proposed FRRNet model uses the adding layers of max pooling (MP) and average pooling (AP), and the proposed model splices the deep network with the shallow network to reduce noise extraction and enhance feature reuse. The FRRNet model is tested on 20 and 47 subjects under the MIT-BIH dataset, and its recognition accuracy is 99.32% and 100%, respectively. Additionally, the FRRNet model is tested on 50 and 87 subjects under the PhysioNet/Computing in Cardiology Challenge 2017 (CinC_2017) dataset, and its recognition accuracy is 94.52% and 93.51%, respectively. A total of 20 subjects are taken from the MIT-BIH and the CinC_2017 datasets for testing, and the recognition accuracy is 98.97%. The experimental results show that the FRRNet model proposed in this paper has high recognition accuracy, and the recognition accuracy is not greatly affected when the number of individuals increases.