A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram.

Autor: Musa N; Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria., Gital AY; Department of Mathematical Sciences, Abubakar Tafawa Balewa University, Bauchi, Nigeria., Aljojo N; University of Jeddah, Jeddah, Saudi Arabia., Chiroma H; Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia.; Computer Science and Engineering , University of Hafr Al-Batin, Hafr Al-Batin, Saudi Arabia., Adewole KS; Department of Computer Science, University of Ilorin, Ilorin, Nigeria., Mojeed HA; Department of Computer Science, University of Ilorin, Ilorin, Nigeria., Faruk N; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Abdulkarim A; Department of Electrical Engineering, Ahmadu Bello University Zaria, Zaria, Nigeria., Emmanuel I; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Folawiyo YY; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Ogunmodede JA; Department of Medicine, University of Ilorin, Ilorin, Nigeria., Oloyede AA; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Olawoyin LA; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Sikiru IA; Department of Physics, Sule Lamido University, Kafin Hausa, Nigeria., Katb I; Computer Science and Engineering, University of Hafr Al-Batin, Hafr, Saudi Arabia.
Jazyk: angličtina
Zdroj: Journal of ambient intelligence and humanized computing [J Ambient Intell Humaniz Comput] 2023; Vol. 14 (7), pp. 9677-9750. Date of Electronic Publication: 2022 Jul 07.
DOI: 10.1007/s12652-022-03868-z
Abstrakt: The success of deep learning over the traditional machine learning techniques in handling artificial intelligence application tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has made deep learning the buzz word that dominates Artificial Intelligence applications. From the last decade, the applications of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. The study shows that the applications of deep learning in ECG have been applied in different domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on biometric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithm.
Supplementary Information: The online version contains supplementary material available at 10.1007/s12652-022-03868-z.
Competing Interests: Conflict of interestNo competing interests are reported by the authors.
(© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.)
Databáze: MEDLINE
Externí odkaz: