Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition
Abstract The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac ele...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-89752-0 |
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| author | Yihang Zhang Hang Zhao |
| author_facet | Yihang Zhang Hang Zhao |
| author_sort | Yihang Zhang |
| collection | DOAJ |
| description | Abstract The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh–Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and $$\varepsilon$$ in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and $$\alpha$$ were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attention modules. The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. After TPE optimization, the deep model’s performance achieved a maximum accuracy of 95.91%. The MIT-BIH arrhythmia database was further utilized for model validation, ascertaining its robustness and generalizability. The proposed deep attention modeling and classification strategy can help in arrhythmia signal processing and may offer inspiration for other signal processing fields as well. |
| format | Article |
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| institution | DOAJ |
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| language | English |
| publishDate | 2025-02-01 |
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| spelling | doaj-art-35b725e68ac6424987ae81663a1a52782025-08-20T02:48:29ZengNature PortfolioScientific Reports2045-23222025-02-0115111910.1038/s41598-025-89752-0Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decompositionYihang Zhang0Hang Zhao1Information Center, Zhejiang Provincial People’s HospitalInformation Center, Zhejiang Provincial People’s HospitalAbstract The detection and classification of arrhythmia play a vital role in the diagnosis and management of cardiac disorders. Many deep learning techniques are utilized for arrhythmia classification in current research but only based on ECG data, lacking the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart based on the FitzHugh–Nagumo (FHN) model was established for cardiac electrophysiology simulation and the ECG signals were acquired from the FEM results of representative points. Two different kinds of arrhythmia characterized by major anomalies of parameters a and $$\varepsilon$$ in the FHN model were simulated, and the synthetic ECG signals were obtained respectively. A multi-objective optimization method based on non-dominated sorting was incorporated into the crayfish optimization algorithm to optimize the key parameters in VMD, then a variational mode decomposition technique for ECG signal processing based on a multi-objective crayfish optimization algorithm (MOCOA-VMD) was proposed, wherein the spectral kurtosis and KL divergence were determined as the indicators for decomposition. The Pareto optimal front was generated by MOCOA and the intrinsic mode functions of VMD with the best combination of K and $$\alpha$$ were obtained. A deep attention model based on MOCOA-VMD was constructed for ECG signal classification. The ablation study was implemented to verify the effectiveness of the proposed signal decomposition method and deep attention modules. The performance of the model based on MOCOA-VMD achieves the best accuracy of 94.35%, much higher than the model constructed by modules of EEMD, VMD and CNN. Moreover, Bayesian optimization was carried out to fine-tune the hyperparameters batch size, learning rate, epochs, and momentum. After TPE optimization, the deep model’s performance achieved a maximum accuracy of 95.91%. The MIT-BIH arrhythmia database was further utilized for model validation, ascertaining its robustness and generalizability. The proposed deep attention modeling and classification strategy can help in arrhythmia signal processing and may offer inspiration for other signal processing fields as well.https://doi.org/10.1038/s41598-025-89752-0Arrthythmia signal classificationAttention schemeFinite element methodCrayfish optimization algorithmVariational mode decompositionBayesian optimization |
| spellingShingle | Yihang Zhang Hang Zhao Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition Scientific Reports Arrthythmia signal classification Attention scheme Finite element method Crayfish optimization algorithm Variational mode decomposition Bayesian optimization |
| title | Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition |
| title_full | Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition |
| title_fullStr | Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition |
| title_full_unstemmed | Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition |
| title_short | Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition |
| title_sort | deep attention model for arrhythmia signal classification based on multi objective crayfish optimization algorithmic variational mode decomposition |
| topic | Arrthythmia signal classification Attention scheme Finite element method Crayfish optimization algorithm Variational mode decomposition Bayesian optimization |
| url | https://doi.org/10.1038/s41598-025-89752-0 |
| work_keys_str_mv | AT yihangzhang deepattentionmodelforarrhythmiasignalclassificationbasedonmultiobjectivecrayfishoptimizationalgorithmicvariationalmodedecomposition AT hangzhao deepattentionmodelforarrhythmiasignalclassificationbasedonmultiobjectivecrayfishoptimizationalgorithmicvariationalmodedecomposition |