A review on deep learning methods for heart sound signal analysis
IntroductionApplication of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic...
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1434022/full |
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| author | Elaheh Partovi Ankica Babic Ankica Babic Arash Gharehbaghi |
| author_facet | Elaheh Partovi Ankica Babic Ankica Babic Arash Gharehbaghi |
| author_sort | Elaheh Partovi |
| collection | DOAJ |
| description | IntroductionApplication of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic picture of the methodological performance from the reported methods.MethodsThis survey paper provides the results of a broad retrospective study on the recent advances in heart sound analysis using DL methods. Representation of the results is performed according to both methodological and applicative taxonomies. The study method covers a wide span of related keywords using well-known search engines. Implementation of the observed methods along with the related results is pervasively represented and compared.Results and discussionIt is observed that convolutional neural networks and recurrent neural networks are the most commonly used ones for discriminating abnormal heart sounds and localization of heart sounds with 67.97% and 33.33% of the related papers, respectively. The convolutional neural network and the autoencoder network show a perfect accuracy of 100% in the case studies on the classification of abnormal from normal heart sounds. Nevertheless, this superiority against other methods with lower accuracy is not conclusive due to the inconsistency in evaluation. |
| format | Article |
| id | doaj-art-4cacabae5ebe4a519b7d87783cb5b687 |
| institution | OA Journals |
| issn | 2624-8212 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-4cacabae5ebe4a519b7d87783cb5b6872025-08-20T02:14:56ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122024-11-01710.3389/frai.2024.14340221434022A review on deep learning methods for heart sound signal analysisElaheh Partovi0Ankica Babic1Ankica Babic2Arash Gharehbaghi3Department of Electrical Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Biomedical Engineering, Linköping University, Linköping, SwedenDepartment of Information Science and Media Studies, University of Bergen, Bergen, NorwayDepartment of Biomedical Engineering, Linköping University, Linköping, SwedenIntroductionApplication of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic picture of the methodological performance from the reported methods.MethodsThis survey paper provides the results of a broad retrospective study on the recent advances in heart sound analysis using DL methods. Representation of the results is performed according to both methodological and applicative taxonomies. The study method covers a wide span of related keywords using well-known search engines. Implementation of the observed methods along with the related results is pervasively represented and compared.Results and discussionIt is observed that convolutional neural networks and recurrent neural networks are the most commonly used ones for discriminating abnormal heart sounds and localization of heart sounds with 67.97% and 33.33% of the related papers, respectively. The convolutional neural network and the autoencoder network show a perfect accuracy of 100% in the case studies on the classification of abnormal from normal heart sounds. Nevertheless, this superiority against other methods with lower accuracy is not conclusive due to the inconsistency in evaluation.https://www.frontiersin.org/articles/10.3389/frai.2024.1434022/fullphonocardiogramintelligent phonocardiographydeep learningheart soundheart sound segmentationheart disease |
| spellingShingle | Elaheh Partovi Ankica Babic Ankica Babic Arash Gharehbaghi A review on deep learning methods for heart sound signal analysis Frontiers in Artificial Intelligence phonocardiogram intelligent phonocardiography deep learning heart sound heart sound segmentation heart disease |
| title | A review on deep learning methods for heart sound signal analysis |
| title_full | A review on deep learning methods for heart sound signal analysis |
| title_fullStr | A review on deep learning methods for heart sound signal analysis |
| title_full_unstemmed | A review on deep learning methods for heart sound signal analysis |
| title_short | A review on deep learning methods for heart sound signal analysis |
| title_sort | review on deep learning methods for heart sound signal analysis |
| topic | phonocardiogram intelligent phonocardiography deep learning heart sound heart sound segmentation heart disease |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1434022/full |
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