ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features
<p>Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challengi...
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| مؤلفون آخرون: | , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513519822569472 |
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| author | Sadia Din (18136919) |
| author2 | Marwa Qaraqe (10135172) Omar Mourad (6553316) Khalid Qaraqe (16896504) Erchin Serpedin (3706543) |
| author2_role | author author author author |
| author_facet | Sadia Din (18136919) Marwa Qaraqe (10135172) Omar Mourad (6553316) Khalid Qaraqe (16896504) Erchin Serpedin (3706543) |
| author_role | author |
| dc.creator.none.fl_str_mv | Sadia Din (18136919) Marwa Qaraqe (10135172) Omar Mourad (6553316) Khalid Qaraqe (16896504) Erchin Serpedin (3706543) |
| dc.date.none.fl_str_mv | 2024-04-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.artmed.2024.102818 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/ECG-based_cardiac_arrhythmias_detection_through_ensemble_learning_and_fusion_of_deep_spatial_temporal_and_long-range_dependency_features/25479955 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence CNN LSTM Transformer Feature fusion |
| dc.title.none.fl_str_mv | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN–LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN–LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.</p><h2>Other Information</h2> <p> Published in: Artificial Intelligence in Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.artmed.2024.102818" target="_blank">https://dx.doi.org/10.1016/j.artmed.2024.102818</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_613c0071e4fb8afbf2ec956096a11b53 |
| identifier_str_mv | 10.1016/j.artmed.2024.102818 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25479955 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency featuresSadia Din (18136919)Marwa Qaraqe (10135172)Omar Mourad (6553316)Khalid Qaraqe (16896504)Erchin Serpedin (3706543)Information and computing sciencesArtificial intelligenceCNNLSTMTransformerFeature fusion<p>Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients. Manual analysis of ECG is time-consuming and challenging. Hence, effective automated detection of heart arrhythmias is important to produce reliable results. Different deep-learning techniques to detect heart arrhythmias such as Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Transformer, and Hybrid CNN–LSTM were proposed. However, these techniques, when used individually, are not sufficient to effectively learn multiple features from the ECG signal. The fusion of CNN and LSTM overcomes the limitations of CNN in the existing studies as CNN–LSTM hybrids can extract spatiotemporal features. However, LSTMs suffer from long-range dependency issues due to which certain features may be ignored. Hence, to compensate for the drawbacks of the existing models, this paper proposes a more comprehensive feature fusion technique by merging CNN, LSTM, and Transformer models. The fusion of these models facilitates learning spatial, temporal, and long-range dependency features, hence, helping to capture different attributes of the ECG signal. These features are subsequently passed to a majority voting classifier equipped with three traditional base learners. The traditional learners are enriched with deep features instead of handcrafted features. Experiments are performed on the MIT-BIH arrhythmias database and the model performance is compared with that of the state-of-art models. Results reveal that the proposed model performs better than the existing models yielding an accuracy of 99.56%.</p><h2>Other Information</h2> <p> Published in: Artificial Intelligence in Medicine<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.artmed.2024.102818" target="_blank">https://dx.doi.org/10.1016/j.artmed.2024.102818</a></p>2024-04-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.artmed.2024.102818https://figshare.com/articles/journal_contribution/ECG-based_cardiac_arrhythmias_detection_through_ensemble_learning_and_fusion_of_deep_spatial_temporal_and_long-range_dependency_features/25479955CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/254799552024-04-01T00:00:00Z |
| spellingShingle | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features Sadia Din (18136919) Information and computing sciences Artificial intelligence CNN LSTM Transformer Feature fusion |
| status_str | publishedVersion |
| title | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| title_full | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| title_fullStr | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| title_full_unstemmed | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| title_short | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| title_sort | ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial–temporal and long-range dependency features |
| topic | Information and computing sciences Artificial intelligence CNN LSTM Transformer Feature fusion |