ECG databases for biometric systems: A systematic review. (January 2017)

• Record Type: Journal Article
• Authors: Merone, Mario; Soda, Paolo; Sansone, Mario; Sansone, Carlo
• Is Part Of: Expert systems with applications. Volume 67(2017)
• Page Start: 189
• Page End: 202
• Publication Date: 2017-01
• Subjects: Biometric -- Electrocardiogram -- Recognition -- Database -- Review -- Computer-based biometric systems; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.09.030
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: We perform a systematic review of ECG biometric databases in the literature. We compare the acquisition hardware used to acquire ECG biometric databases. We compare the acquisition protocols adopted by ECG biometric databases. We highlight that an exhaustive database for biometric studies still lacks. Abstract: Computer-based biometric systems (CBBSs) individual recognition are expert and intelligent systems that are gaining increasing interest in many areas, such as securing financial systems, telecommunications and healthcare applications. The electrocardiogram (ECG) has been used as biometric feature for its low circumvention, large acceptability and uniqueness, thus being at the basis of several CBBSs. As ECG databases collected for clinical applications are not adequate for biometric applications, we have assisted to the development of other repositories of ECG, each one different from the others and highlighting certain issues of ECG-based biometric recognition. Through a systematic framework presented here, we quantitative analyse, evaluate and compare the acquisition hardware and the acquisition protocols of ECG databases available in literature and suited to develop CBBSs. Although the most recent ones, namely CYBHI and UofTDB, result the best for the acquisition hardware and the acquisition protocols, respectively, our survey shows that none is exhaustive for developing a robust and general enough CBBSs. The analysis also highlights the current lack of standardization in this field and the difficulty of performing an effective benchmarking activity. Since a publicly available database is essential for the research community in ECG-based CBBS to correctly assess the performance of existing algorithms or even commercial expert systems, we also discuss here the main features that an "optimal" repository for the intelligent application at hand.