Research Area:  Machine Learning

Abstract:

Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according to the detected R peaks to enable feature extraction from the ECGs of duration equal to nearly one cardiac cycle. Beat averaging should also be applied to reduce the influence of inter-beat variation on the extracted features and identification accuracy. Either detecting R peaks or collecting extra heartbeats for averaging will inevitably lead to a delay in the identification process. This paper proposes a deep learning-based ECG biometric identification scheme that allows identity recognition using a random ECG segment without needing R-peak detection and beat averaging. Moreover, the problem of being vulnerable to unregistered subjects in an identification system is also addressed. Experimental results demonstrated that an identification rate of 99.1% for an identification system having 235 enrollees with an equal error rate of 8.08% was achieved.

Keywords:  

Author(s) Name:   Jui-Kun Chiu; Chun-Shun Chang; Shun-Chi Wu

Publisher name:  IEEE

DOI:  10.1109/EMBC46164.2021.9630899

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/9630899