Publication: Generalized electrocardiogram biometrics based on encoder representations from transformer with augmented dataset generation
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering | |
| dc.contributor.author | Chee, Kai Jye | |
| dc.date.accessioned | 2025-11-27T21:46:47Z | |
| dc.date.available | 2025-11-27T21:46:47Z | |
| dc.date.issued | 2024-10-01 | |
| dc.description.abstract | Electrocardiogram (ECG) biometrics enhances security by complementing other identity proving methods, due to the inherent difficulty of falsifying an internal signal. Traditional machine learning approaches have various limitations, including issues like inadequate training data and overlooking the dependencies between enrolment and query segments. Furthermore, conventional identification classifiers face a trade-off between accuracy and adaptability to enrolment changes, often requiring retraining upon new enrolments. This research introduces Multi-database Training Examples Generation (MTEG) to generate abundant training datasets from multiple ECG databases. This research also presents the Enrolled-Query ECG Pair Feature Extractor (EQFE) to extract inter-segment dependencies of the enrolment and query segments, and Self-Attention Identification Classifier (SAIC) that considers all enrolment subjects in the identification task, while also accommodating changes to the enrolment subjects without necessitating model retraining. The incorporation of ten databases in MTEG improves authentication accuracy from 71.54% to 99.57% in the PTB Diagnostic ECG Database, compared to using a single database. Furthermore, the presence of EQFE enhances authentication accuracy from 70.94% to 98.12% in the Stress Test Database, relative to its absence. Additionally, the utilization of SAIC results in a notable improvement in identification accuracy, rising from 92.87% to 96.29% in the Atrial Fibrillation Database, compared to its absence. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/23274 | |
| dc.language.iso | en | |
| dc.title | Generalized electrocardiogram biometrics based on encoder representations from transformer with augmented dataset generation | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |