SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network
<p dir="ltr">Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these challenges, we propose a...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513532041625600 |
|---|---|
| author | Samin Yaser (22803530) |
| author2 | Muhammad E.H. Chowdhury (17151154) Rusab Sarmun (17632269) |
| author2_role | author author |
| author_facet | Samin Yaser (22803530) Muhammad E.H. Chowdhury (17151154) Rusab Sarmun (17632269) |
| author_role | author |
| dc.creator.none.fl_str_mv | Samin Yaser (22803530) Muhammad E.H. Chowdhury (17151154) Rusab Sarmun (17632269) |
| dc.date.none.fl_str_mv | 2025-06-27T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2025.110536 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/SER_inspired_deep_learning_approach_to_detect_cardiac_arrhythmias_in_electrocardiogram_signals_using_Temporal_Convolutional_Network_and_graph_neural_network/30819575 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Cardiac arrhythmia Deep learning Residual Temporal Convolutional Network Feature map Graph neural network Signal processing Electrocardiogram (ECG) Classification model |
| dc.title.none.fl_str_mv | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these challenges, we propose a novel <u>deep learning</u> approach that combines Temporal Convolutional Networks (TCN) with Graph Convolutional Networks (GCN) for robust cardiac <u>arrhythmia</u> detection. Unlike conventional methods, our approach constructs cycle graphs from ECG signals, leveraging graph signal processing to enhance data representation and improve classification accuracy. The proposed model processes raw ECG signals using a TCN-based feature extractor, followed by spectral graph convolutions via a GCN. We evaluate our model using the Chapman and Shaoxing 12-lead ECG database, consolidating 11 rhythm classes into four superclasses for improved classification reliability. Our model achieves a high classification accuracy of 95.5%, surpassing standard GCNs and other deep learning architectures while significantly reducing graph construction complexity. These results demonstrate the potential of graph-based deep learning for efficient and accurate ECG analysis in resource-constrained medical settings.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and 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.compbiomed.2025.110536" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.110536</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a001250fa316a139dae64747dadcc0bd |
| identifier_str_mv | 10.1016/j.compbiomed.2025.110536 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30819575 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural networkSamin Yaser (22803530)Muhammad E.H. Chowdhury (17151154)Rusab Sarmun (17632269)Biomedical and clinical sciencesCardiovascular medicine and haematologyEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningCardiac arrhythmiaDeep learningResidual Temporal Convolutional NetworkFeature mapGraph neural networkSignal processingElectrocardiogram (ECG)Classification model<p dir="ltr">Electrocardiogram (ECG) signals play a critical role in diagnosing cardiovascular diseases (CVDs), yet automated ECG classification remains challenging due to inter-patient variability, signal noise, and heart rhythm complexity. To address these challenges, we propose a novel <u>deep learning</u> approach that combines Temporal Convolutional Networks (TCN) with Graph Convolutional Networks (GCN) for robust cardiac <u>arrhythmia</u> detection. Unlike conventional methods, our approach constructs cycle graphs from ECG signals, leveraging graph signal processing to enhance data representation and improve classification accuracy. The proposed model processes raw ECG signals using a TCN-based feature extractor, followed by spectral graph convolutions via a GCN. We evaluate our model using the Chapman and Shaoxing 12-lead ECG database, consolidating 11 rhythm classes into four superclasses for improved classification reliability. Our model achieves a high classification accuracy of 95.5%, surpassing standard GCNs and other deep learning architectures while significantly reducing graph construction complexity. These results demonstrate the potential of graph-based deep learning for efficient and accurate ECG analysis in resource-constrained medical settings.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and 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.compbiomed.2025.110536" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.110536</a></p>2025-06-27T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2025.110536https://figshare.com/articles/journal_contribution/SER_inspired_deep_learning_approach_to_detect_cardiac_arrhythmias_in_electrocardiogram_signals_using_Temporal_Convolutional_Network_and_graph_neural_network/30819575CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308195752025-06-27T12:00:00Z |
| spellingShingle | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network Samin Yaser (22803530) Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Cardiac arrhythmia Deep learning Residual Temporal Convolutional Network Feature map Graph neural network Signal processing Electrocardiogram (ECG) Classification model |
| status_str | publishedVersion |
| title | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| title_full | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| title_fullStr | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| title_full_unstemmed | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| title_short | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| title_sort | SER inspired deep learning approach to detect cardiac arrhythmias in electrocardiogram signals using Temporal Convolutional Network and graph neural network |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Cardiac arrhythmia Deep learning Residual Temporal Convolutional Network Feature map Graph neural network Signal processing Electrocardiogram (ECG) Classification model |