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- W4200249265 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." @default.
- W4200249265 created "2021-12-31" @default.
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- W4200249265 date "2021-11-01" @default.
- W4200249265 modified "2023-09-24" @default.
- W4200249265 title "ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach" @default.
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- W4200249265 doi "https://doi.org/10.1109/embc46164.2021.9630899" @default.
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