Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.
Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences featu...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0318070 |
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| _version_ | 1825206799634006016 |
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| author | Qiao Xiao Chaofeng Wang |
| author_facet | Qiao Xiao Chaofeng Wang |
| author_sort | Qiao Xiao |
| collection | DOAJ |
| description | Electrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences feature quality and diagnostic accuracy. Traditional methods typically rely on fixed wavelet bases chosen heuristically or through trial-and-error, which can fail to cover the distinct characteristics of individual ECG signals, leading to suboptimal performance. To address this limitation, we propose a reinforcement learning-based wavelet base selection (RLWBS) framework that dynamically customizes the wavelet base for each ECG signal. In this framework, a reinforcement learning (RL) agent iteratively optimizes its wavelet base selection (WBS) strategy based on successive feedback of classification performance, aiming to achieve progressively optimized feature extraction. Experiments conducted on the clinically collected PTB-XL dataset for ECG abnormality classification show that the proposed RLWBS framework could obtain more detailed time-frequency representation of ECG signals, yielding enhanced diagnostic performance compared to traditional WBS approaches. |
| format | Article |
| id | doaj-art-845feecda21a4476bfc6a5d10bc1bb8b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-845feecda21a4476bfc6a5d10bc1bb8b2025-02-07T05:30:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031807010.1371/journal.pone.0318070Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach.Qiao XiaoChaofeng WangElectrocardiogram (ECG) signals are crucial in diagnosing cardiovascular diseases (CVDs). While wavelet-based feature extraction has demonstrated effectiveness in deep learning (DL)-based ECG diagnosis, selecting the optimal wavelet base poses a significant challenge, as it directly influences feature quality and diagnostic accuracy. Traditional methods typically rely on fixed wavelet bases chosen heuristically or through trial-and-error, which can fail to cover the distinct characteristics of individual ECG signals, leading to suboptimal performance. To address this limitation, we propose a reinforcement learning-based wavelet base selection (RLWBS) framework that dynamically customizes the wavelet base for each ECG signal. In this framework, a reinforcement learning (RL) agent iteratively optimizes its wavelet base selection (WBS) strategy based on successive feedback of classification performance, aiming to achieve progressively optimized feature extraction. Experiments conducted on the clinically collected PTB-XL dataset for ECG abnormality classification show that the proposed RLWBS framework could obtain more detailed time-frequency representation of ECG signals, yielding enhanced diagnostic performance compared to traditional WBS approaches.https://doi.org/10.1371/journal.pone.0318070 |
| spellingShingle | Qiao Xiao Chaofeng Wang Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. PLoS ONE |
| title | Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. |
| title_full | Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. |
| title_fullStr | Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. |
| title_full_unstemmed | Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. |
| title_short | Adaptive wavelet base selection for deep learning-based ECG diagnosis: A reinforcement learning approach. |
| title_sort | adaptive wavelet base selection for deep learning based ecg diagnosis a reinforcement learning approach |
| url | https://doi.org/10.1371/journal.pone.0318070 |
| work_keys_str_mv | AT qiaoxiao adaptivewaveletbaseselectionfordeeplearningbasedecgdiagnosisareinforcementlearningapproach AT chaofengwang adaptivewaveletbaseselectionfordeeplearningbasedecgdiagnosisareinforcementlearningapproach |