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

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dc.contributor.authorMusa, N
dc.contributor.authorGital, A. Y.
dc.contributor.authorAljojo, N.
dc.contributor.authorChiroma, H.
dc.contributor.authorAdewole, K. S.
dc.contributor.authorMojeed, H. A.
dc.contributor.authorFaruk, N.
dc.contributor.authorAbdulkarim, A.
dc.contributor.authorEmmanuel, I.
dc.contributor.authorFolawiyo, Y. Y.
dc.contributor.authorOgunmodede, J. A.
dc.contributor.authorOloyede, A. A.
dc.contributor.authorOlawoyin, L. A.
dc.contributor.authorSikiru, I. A.
dc.contributor.authorKatibi, I. A.
dc.date.accessioned2023-06-26T08:51:05Z
dc.date.available2023-06-26T08:51:05Z
dc.date.issued2022
dc.description.abstractAbstract tasks such as image processing, computer vision, object detection, speech recognition, medical imaging and so on, has based systems of the applications of deep learning in ECG with respect to domain of applications. To address this gap, The study shows that the applications of deep learning in ECG have been applied in diferent domains. We presented a new taxonomy of the domains of application of the deep learning in ECG. The paper also presented discussions on bio novel research. We believe that this study will be useful to both new researchers and expert researchers who are seeking However, previous surveys have not been able to provide a systematic comprehensive review including biometric ECG metric ECG based systems and meta-data analysis of the studies based on the domain, area, task, deep learning models, we conducted a systematic literature review on the applications of deep learning in ECG including biometric ECG based The success of deep learning over the traditional machine learning techniques in handling artifcial intelligence application dataset sources and preprocessing methods. Challenges and potential research opportunities were highlighted to enable tions of deep learning in physiological signals such as electrocardiogram (ECG) have attracted a good number of research. made deep learning the buzz word that dominates Artifcial Intelligence applications. From the last decade, the applica systems. The study analyzed systematically, 150 primary studies with evidence of the application of deep learning in ECG. to add knowledge to the already existing body of knowledge in ECG signal processing using deep learning algorithmen_US
dc.identifier.urihttps://doi.org/10.1007/s1265202203868z
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11269
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectBiometric Electrocardiogram System· Machine learning· Deep learning· Electrocardiogram· Driving,en_US
dc.titleA systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogramen_US
dc.typeArticleen_US

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