ECG-Based Biometric Recognition: A Survey of Methods and Databases

Meltzer Camino, David ORCID: https://orcid.org/0000-0003-3074-9023 and Luengo García, David ORCID: https://orcid.org/0000-0001-7407-3630 (2025). ECG-Based Biometric Recognition: A Survey of Methods and Databases. "Sensors", v. 25 (n. 6); p. 1864. ISSN 14248220. https://doi.org/10.3390/s25061864.

Descripción

Título: ECG-Based Biometric Recognition: A Survey of Methods and Databases
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Sensors
Fecha: 17 Marzo 2025
ISSN: 14248220
Volumen: 25
Número: 6
Materias:
ODS:
Palabras Clave Informales: ECG biometrics, identification, verification, ECG databases, survey
Escuela: E.T.S.I. y Sistemas de Telecomunicación (UPM)
Departamento: Ingeniería Telemática y Electrónica
Licencias Creative Commons: Reconocimiento

Texto completo

[thumbnail of 10344197.pdf] PDF (Portable Document Format) - Se necesita un visor de ficheros PDF, como GSview, Xpdf o Adobe Acrobat Reader
Descargar (670kB)

Resumen

This work presents a comprehensive and chronologically ordered survey of existing studies and data sources on Electrocardiogram (ECG) based biometric recognition systems. This survey is organized in terms of the two main goals pursued in it: first, a description of the main ECG features and recognition techniques used in the existing literature, including a comprehensive compilation of references; second, a survey of the ECG databases available and used by the referenced studies. The most relevant characteristics of the databases are identified, and a comprehensive compilation of databases is given. To date, no other work has presented such a complete overview of both studies and data sources for ECG-based biometric recognition. Readers interested in the subject can obtain an understanding of the state of the art, easily identifying specific key papers by using different criteria, and become aware of the databases where they can test their novel algorithms.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Gobierno de España
PID2023-153035NB-I00
Sin especificar
Sin especificar
REdes PROfundas para Detección Explicable de Anomalías en SEries temporales

Más información

ID de Registro: 95017
Identificador DC: https://oa.upm.es/95017/
Identificador OAI: oai:oa.upm.es:95017
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/10344197
Identificador DOI: 10.3390/s25061864
URL Oficial: https://www.mdpi.com/3228626
Depositado por: iMarina Portal Científico
Depositado el: 23 Mar 2026 16:40
Ultima Modificación: 23 Mar 2026 16:40