Heartprint: A Dataset of Multisession ECG Signal with Long Interval Captured from Fingers for Biometric Recognition

Md Saiful Islam (), Haikel Alhichri, Yakoub Bazi, Nassim Ammour, Naif Alajlan and Rami M. Jomaa
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Md Saiful Islam: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Haikel Alhichri: Advanced Laboratory for Intelligent Systems Research (ALISR), Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Yakoub Bazi: Advanced Laboratory for Intelligent Systems Research (ALISR), Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Nassim Ammour: Advanced Laboratory for Intelligent Systems Research (ALISR), Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Naif Alajlan: Advanced Laboratory for Intelligent Systems Research (ALISR), Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Rami M. Jomaa: Artificial Intelligence Department, College of Computer and Cyber Sciences, University of Prince Mugrin, Medina 42241, Saudi Arabia

Data, 2022, vol. 7, issue 10, 1-16

Abstract: The electrocardiogram (ECG) signal produced by the human heart is an emerging biometric modality that can play an important role in the future generation’s identity recognition with the support of machine learning techniques. One of the major obstacles in the progress of this modality is the lack of public datasets with a long interval between sessions of data acquisition to verify the uniqueness and permanence of the biometric signature of the heart of a subject. To address this issue, we put forward Heartprint, a large biometric database of multisession ECG signals comprising 1539 records captured from the fingers of 199 healthy subjects. The capturing time for each record was 15 s, and recordings were made in resting and reading conditions. They were collected in multiple sessions over ten years, and the average interval between first session (S1) and third session (S3L) was 1572.2 days. The dataset also covers several demographic classes such as genders, ethnicities, and age groups. The combination of raw ECG signals and demographic information turns the Heartprint dataset, which is made publicly available online, into a valuable resource for the development and evaluation of biometric recognition algorithms.

Keywords: biometrics; electrocardiogram signal; machine learning; multisession dataset; identification; authentication (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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