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

ABSTRACT: 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 extractor, inspired by Bidirectional Encoder Representations from Transformers (BERT)'s sentence pair task, to obtain a dynamic representation of a pair of ECGs. We also propose using the self-attention mechanism of the transformer to draw an inter-identity relationship when performing ECG identification tasks. The model was trained once with datasets built from 10 ECG databases, and then, it was applied to six other ECG databases without retraining. We emphasize the significance of the time separation

SUBMITTER: Chee KJ 

PROVIDER: S-EPMC9100332 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature