BioECG: Improving ECG biometrics with deep learning and enhanced datasets

Tirado Martín, Paloma; Sanchez-Reillo, Raul

Publication:
BioECG: Improving ECG biometrics with deep learning and enhanced datasets

dc.affiliation.dpto	UC3M. Departamento de Tecnología Electrónica	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: Universitario de Tecnologías de Identificación (GUTI)	es
dc.contributor.author	Tirado Martín, Paloma
dc.contributor.author	Sanchez-Reillo, Raul
dc.date.accessioned	2021-12-20T10:19:50Z
dc.date.available	2021-12-20T10:19:50Z
dc.date.issued	2021-07-01
dc.description.abstract	Nowadays, Deep Learning tools have been widely applied in biometrics. Electrocardiogram (ECG) biometrics is not the exception. However, the algorithm performances rely heavily on a representative dataset for training. ECGs suffer constant temporal variations, and it is even more relevant to collect databases that can represent these conditions. Nonetheless, the restriction in database publications obstructs further research on this topic. This work was developed with the help of a database that represents potential scenarios in biometric recognition as data was acquired in different days, physical activities and positions. The classification was implemented with a Deep Learning network, BioECG, avoiding complex and time-consuming signal transformations. An exhaustive tuning was completed including variations in enrollment length, improving ECG verification for more complex and realistic biometric conditions. Finally, this work studied one-day and two-days enrollments and their effects. Two-days enrollments resulted in huge general improvements even when verification was accomplished with more unstable signals. EER was improved in 63% when including a change of position, up to almost 99% when visits were in a different day and up to 91% if the user experienced a heartbeat increase after exercise.	en
dc.format.extent	20
dc.identifier.bibliographicCitation	Tirado-Martin, P. & Sanchez-Reillo, R. (2021). BioECG: Improving ECG Biometrics with Deep Learning and Enhanced Datasets. Applied Sciences, 11(13), 5880.	en
dc.identifier.doi	https://doi.org/10.3390/app11135880
dc.identifier.issn	2076-3417
dc.identifier.publicationfirstpage	5880
dc.identifier.publicationissue	13
dc.identifier.publicationtitle	Applied Sciences	en
dc.identifier.publicationvolume	11
dc.identifier.uri	https://hdl.handle.net/10016/33800
dc.identifier.uxxi	AR/0000028865
dc.language.iso	eng
dc.publisher	MDPI	en
dc.rights	© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Electrónica	es
dc.subject.other	ECG biometrics	en
dc.subject.other	Deep learning	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Human verification	en
dc.subject.other	Convolutional neural networks	en
dc.subject.other	Long-short term memory	en
dc.title	BioECG: Improving ECG biometrics with deep learning and enhanced datasets	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication