Zhao Hang, Yin Xiongfei
School of Physics and Optoelectronic Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
PLoS One. 2025 May 14;20(5):e0321484. doi: 10.1371/journal.pone.0321484. eCollection 2025.
Recent research for arrhythmia classification is increasingly based on AI-driven approaches, which are primarily grounded in ECG data, but often neglect the mathematical foundations of cardiac electrophysiology. A finite element model (FEM) of the human heart, grounded in the Hodgkin-Huxley (HH) model was established to simulate cardiac electrophysiology, and ECG signals from 200 representative points were acquired. Two types of arrhythmia characterized by significant anomalies in the variables of the HH model were simulated, and corresponding synthetic ECG signals were generated. A multi-objective optimization method based on non-dominated sorting was integrated into the crayfish optimization algorithm (MOCOA). To optimize the key parameters K and [Formula: see text] in variational mode decomposition (VMD), a MOCOA-VMD technique specifically tailored for ECG signal processing was developed. The Pareto optimal front was generated using MOCOA with the indicators of spectral kurtosis and KL divergence, by which the optimal intrinsic mode functions were obtained. A deep VMD-attention network based on MOCOA was developed for ECG signal classification. The ablation study evaluated the effectiveness of the proposed signal decomposition method and deep attention modules. The model based on MOCOA-VMD achieves the highest accuracy of 94.46%, outperforming models constructed using EEMD, VMD, CNN and LSTM modules. Bayesian optimization was employed to fine-tune the hyperparameters and further enhance the performance of the deep model, with the best accuracy of the deep attention model after TPE optimization reaching 96.11%. Moreover, the real-world MIT-BIH arrhythmia database was utilized for further validation to prove the robustness and generalizability of the proposed model. The proposed deep VMD-attention modeling and classification strategy has shown significant promise and may offer valuable inspiration for other signal processing fields as well.
最近关于心律失常分类的研究越来越多地基于人工智能驱动的方法，这些方法主要基于心电图数据，但往往忽视了心脏电生理学的数学基础。建立了基于霍奇金-赫胥黎（HH）模型的人体心脏有限元模型（FEM）来模拟心脏电生理学，并采集了来自200个代表性点的心电图信号。模拟了两种以HH模型变量显著异常为特征的心律失常，并生成了相应的合成心电图信号。将基于非支配排序的多目标优化方法集成到小龙虾优化算法（MOCOA）中。为了优化变分模态分解（VMD）中的关键参数K和[公式：见原文]，开发了一种专门为心电图信号处理量身定制的MOCOA-VMD技术。使用MOCOA以谱峰度和KL散度为指标生成帕累托最优前沿，由此获得最优本征模态函数。开发了一种基于MOCOA的深度VMD-注意力网络用于心电图信号分类。消融研究评估了所提出的信号分解方法和深度注意力模块的有效性。基于MOCOA-VMD的模型实现了94.46%的最高准确率，优于使用EEMD、VMD、CNN和LSTM模块构建的模型。采用贝叶斯优化对超参数进行微调，进一步提高深度模型的性能，经树状帕累托估计优化后深度注意力模型的最佳准确率达到96.11%。此外，利用真实世界的麻省理工学院-贝斯以色列女执事医疗中心心律失常数据库进行进一步验证，以证明所提出模型的鲁棒性和通用性。所提出的深度VMD-注意力建模和分类策略已显示出巨大的前景，也可能为其他信号处理领域提供有价值的灵感。
