resumo
In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.
palavras-chave
INDIVIDUAL IDENTIFICATION; ECG SIGNALS; AUTHENTICATION; FEATURES; INTERVAL; EEG
categoria
Chemistry; Engineering; Instruments & Instrumentation
autores
Pereira, TMC; Conceiçao, RC; Sencadas, V; Sebastiao, R
nossos autores
Vitor Sencadas
Professor associadoProjectos
CICECO - Aveiro Institute of Materials (UIDB/50011/2020)
Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)
Collaboratory for Emerging Technologies, CoLab (EMERGING TECHNOLOGIES)
agradecimentos
This work was funded by national funds through FCT-Fundacao para a Ciencia e a Tecnologia, I.P., under the Scientific Employment Stimulus-Individual Call-CEECIND/03986/2018 (R.S.) and the PhD grant UI/BD/153605/2022 (T.M.C.P.), and is also supported by the FCT through national funds, within IEETA/UA R&D unit UIDB/00127/2020, IBEB Strategic Program UIDB/00645/2020, and CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 and LA/P/0006/2020 (PIDDAC). This work was co-funded by the European Regional Development Fund (FEDER), through Portugal 2020, under the Operational Competitiveness and Internationalization (COMPETE 2020) and Lisboa 2020 programs (grants no. 069918 LISBOA-01-0247-FEDER-069918 POCI-01-0247-FEDER-069918, "CardioLeather"), and CENTRO 2020 program CENTRO-01-0247FEDER-113480 - "ELIPFOOTSENSE".