Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders
Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and plays a crucial role in diagnosing heart disease and assessing cardiac function. In the context of infectious diseases, ECG classification is particularly critical, as many infections, such as viral myo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1543417/full |
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| author | Mei Wang Cong You Wei Zhang Zibo Xu Qi Liang Qiang Li |
| author_facet | Mei Wang Cong You Wei Zhang Zibo Xu Qi Liang Qiang Li |
| author_sort | Mei Wang |
| collection | DOAJ |
| description | Electrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and plays a crucial role in diagnosing heart disease and assessing cardiac function. In the context of infectious diseases, ECG classification is particularly critical, as many infections, such as viral myocarditis and sepsis, can cause significant cardiac complications. Early detection of infection-induced cardiac abnormalities through ECG can provide timely intervention and improve patient outcomes. However, current ECG processing methods often overlook the impact of confounding factors caused by statistical associations, which can compromise classification accuracy, especially in infection-related cardiac conditions. To address this, we propose an innovative approach to causal reasoning based on attention mechanisms. By employing backdoor adjustment for each cardiac lead, our method effectively eliminates confounding factors and models the true causal relationship between ECG patterns and underlying cardiac abnormalities caused by infectious diseases. Furthermore, our approach integrates the concept of entropy with causal inference to enhance ECG classification. By quantifying the information content and variability in ECG signals, we can better identify patterns and anomalies associated with infection-induced cardiac conditions. Experimental results demonstrate that our method achieves significant improvements in classification accuracy and robustness across four benchmark ECG datasets, outperforming existing methods. This work provides a novel perspective on the interplay between infection and cardiac function, offering valuable insights into the detection and understanding of infection-related cardiac complications. |
| format | Article |
| id | doaj-art-4e14388fb0f84836a7d2289a24f5c511 |
| institution | DOAJ |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-4e14388fb0f84836a7d2289a24f5c5112025-08-20T03:07:54ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-05-011610.3389/fphys.2025.15434171543417Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disordersMei Wang0Cong You1Wei Zhang2Zibo Xu3Qi Liang4Qiang Li5Department of Dermatology, Tianjin First Central Hospital, Tianjin, ChinaDepartment of Dermatology and Venereology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, ChinaResearch Department 3, Nanjing Research Institute of Electronic Engineering, Nanjing, ChinaSchool of Microelectronics, Tianjin University, Tianjin, ChinaTianjin Navigation Instruments Research Institute, Tianjin, ChinaSchool of Microelectronics, Tianjin University, Tianjin, ChinaElectrocardiogram (ECG) is a graphical representation of the electrical activity of the heart and plays a crucial role in diagnosing heart disease and assessing cardiac function. In the context of infectious diseases, ECG classification is particularly critical, as many infections, such as viral myocarditis and sepsis, can cause significant cardiac complications. Early detection of infection-induced cardiac abnormalities through ECG can provide timely intervention and improve patient outcomes. However, current ECG processing methods often overlook the impact of confounding factors caused by statistical associations, which can compromise classification accuracy, especially in infection-related cardiac conditions. To address this, we propose an innovative approach to causal reasoning based on attention mechanisms. By employing backdoor adjustment for each cardiac lead, our method effectively eliminates confounding factors and models the true causal relationship between ECG patterns and underlying cardiac abnormalities caused by infectious diseases. Furthermore, our approach integrates the concept of entropy with causal inference to enhance ECG classification. By quantifying the information content and variability in ECG signals, we can better identify patterns and anomalies associated with infection-induced cardiac conditions. Experimental results demonstrate that our method achieves significant improvements in classification accuracy and robustness across four benchmark ECG datasets, outperforming existing methods. This work provides a novel perspective on the interplay between infection and cardiac function, offering valuable insights into the detection and understanding of infection-related cardiac complications.https://www.frontiersin.org/articles/10.3389/fphys.2025.1543417/fullECG classificationattentiontime domain featurescausal reasoningbackdoor adjustmentinfectious disease diagnosis |
| spellingShingle | Mei Wang Cong You Wei Zhang Zibo Xu Qi Liang Qiang Li Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders Frontiers in Physiology ECG classification attention time domain features causal reasoning backdoor adjustment infectious disease diagnosis |
| title | Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders |
| title_full | Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders |
| title_fullStr | Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders |
| title_full_unstemmed | Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders |
| title_short | Causal ECGNet: leveraging causal inference for robust ECG classification in cardiac disorders |
| title_sort | causal ecgnet leveraging causal inference for robust ecg classification in cardiac disorders |
| topic | ECG classification attention time domain features causal reasoning backdoor adjustment infectious disease diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1543417/full |
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