State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
BackgroundElectrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagno...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-08-01
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| Series: | JMIR Medical Informatics |
| Online Access: | https://medinform.jmir.org/2022/8/e38454 |
| _version_ | 1797734832035332096 |
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| author | Georgios Petmezas Leandros Stefanopoulos Vassilis Kilintzis Andreas Tzavelis John A Rogers Aggelos K Katsaggelos Nicos Maglaveras |
| author_facet | Georgios Petmezas Leandros Stefanopoulos Vassilis Kilintzis Andreas Tzavelis John A Rogers Aggelos K Katsaggelos Nicos Maglaveras |
| author_sort | Georgios Petmezas |
| collection | DOAJ |
| description |
BackgroundElectrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals.
ObjectiveThis study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications.
MethodsThe PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches.
ResultsWe identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models.
ConclusionsWe expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making. |
| first_indexed | 2024-03-12T12:49:12Z |
| format | Article |
| id | doaj.art-17e9debfef874515b152f91e88c9c365 |
| institution | Directory Open Access Journal |
| issn | 2291-9694 |
| language | English |
| last_indexed | 2024-03-12T12:49:12Z |
| publishDate | 2022-08-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Medical Informatics |
| spelling | doaj.art-17e9debfef874515b152f91e88c9c3652023-08-28T22:53:05ZengJMIR PublicationsJMIR Medical Informatics2291-96942022-08-01108e3845410.2196/38454State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic ReviewGeorgios Petmezashttps://orcid.org/0000-0002-3371-569XLeandros Stefanopouloshttps://orcid.org/0000-0002-2682-5639Vassilis Kilintzishttps://orcid.org/0000-0002-9783-6757Andreas Tzavelishttps://orcid.org/0000-0002-0750-5007John A Rogershttps://orcid.org/0000-0002-2980-3961Aggelos K Katsaggeloshttps://orcid.org/0000-0003-4554-0070Nicos Maglaverashttps://orcid.org/0000-0002-4919-0664 BackgroundElectrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. ObjectiveThis study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. MethodsThe PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. ResultsWe identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. ConclusionsWe expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.https://medinform.jmir.org/2022/8/e38454 |
| spellingShingle | Georgios Petmezas Leandros Stefanopoulos Vassilis Kilintzis Andreas Tzavelis John A Rogers Aggelos K Katsaggelos Nicos Maglaveras State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review JMIR Medical Informatics |
| title | State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review |
| title_full | State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review |
| title_fullStr | State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review |
| title_full_unstemmed | State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review |
| title_short | State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review |
| title_sort | state of the art deep learning methods on electrocardiogram data systematic review |
| url | https://medinform.jmir.org/2022/8/e38454 |
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