Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification

Electrocardiogram Biometrics Using Transformer’s Self-Attention Mechanism for Sequence Pair Feature Extractor and Flexible Enrollment Scope Identification

The existing electrocardiogram (ECG) biometrics do not perform well when ECG changes after the enrollment phase because the feature extraction is not able to relate ECG collected during enrollment and ECG collected during classification. In this research, we propose the sequence pair feature extract...

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Bibliographic Details
Main Authors: Kai Jye Chee, Dzati Athiar Ramli
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Sensors
Subjects:
transformer
BERT
ECG biometrics
self-attention mechanism
deep learning
multi-class classification
Online Access:https://www.mdpi.com/1424-8220/22/9/3446

Internet

https://www.mdpi.com/1424-8220/22/9/3446

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