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

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 Artifici...

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Published in:Journal of ambient intelligence and humanized computing Vol. 14; no. 7; pp. 9677 - 9750
Main Authors: Musa, Nehemiah, Gital, Abdulsalam Ya’u, Aljojo, Nahla, Chiroma, Haruna, Adewole, Kayode S., Mojeed, Hammed A., Faruk, Nasir, Abdulkarim, Abubakar, Emmanuel, Ifada, Folawiyo, Yusuf Y., Ogunmodede, James A., Oloyede, Abdukareem A., Olawoyin, Lukman A., Sikiru, Ismaeel A., Katb, Ibrahim
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2023
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Big Data
Biometrics
Computational Intelligence
Computer vision
Data analysis
Deep learning
Electrocardiography
Engineering
Health care
Heart
Image processing
Literature reviews
Machine learning
Medical imaging
Neural networks
Object recognition
Original Research
Popularity
Robotics and Automation
Signal processing
Speech recognition
Systematic review
Systems analysis
Taxonomy
Trends
User Interfaces and Human Computer Interaction
Deep learning
Driving
Biometric Electrocardiogram System
Electrocardiogram
Machine learning
ISSN:1868-5137, 1868-5145
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Abstract 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.
AbstractList 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.
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. The online version contains supplementary material available at 10.1007/s12652-022-03868-z.
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.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.The online version contains supplementary material available at 10.1007/s12652-022-03868-z.Supplementary informationThe online version contains supplementary material available at 10.1007/s12652-022-03868-z.
Author Abdulkarim, Abubakar
Emmanuel, Ifada
Gital, Abdulsalam Ya’u
Katb, Ibrahim
Musa, Nehemiah
Oloyede, Abdukareem A.
Ogunmodede, James A.
Olawoyin, Lukman A.
Faruk, Nasir
Sikiru, Ismaeel A.
Chiroma, Haruna
Adewole, Kayode S.
Folawiyo, Yusuf Y.
Aljojo, Nahla
Mojeed, Hammed A.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/35821879$$D View this record in MEDLINE/PubMed
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Copyright The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.
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Issue 7
Keywords Deep learning
Driving
Biometric Electrocardiogram System
Electrocardiogram
Machine learning
Language English
License The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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SubjectTerms Algorithms
Artificial Intelligence
Big Data
Biometrics
Computational Intelligence
Computer vision
Data analysis
Deep learning
Electrocardiography
Engineering
Health care
Heart
Image processing
Literature reviews
Machine learning
Medical imaging
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Title A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram
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