Chang Yi, Dong Ming, Fan Lihong, Kang Bochao, Sun Weikai, Li Xiaofeng, Yang Zhang, Ren Ming
School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.
The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China.
BMC Cardiovasc Disord. 2025 Apr 28;25(1):335. doi: 10.1186/s12872-025-04728-2.
The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to few disease types without sensitivity and specificity. Cardiac electrophysiologic imaging (ECGI) technology reflects cardiac activities accurately and non-invasively, which is of great significance for the diagnosis and treatment of cardiac diseases. This paper aims to provide a new solution for the realization of ECGI by combining simulation model and deep learning methods.
A complete three-dimensional bidomain cardiac electrophysiologic activity model was constructed, and simulated electrocardiogram data were obtained as training samples. Particle swarm optimization-back propagation neural network, convolutional neural network, and long short-term memory network were used respectively to reconstruct the cardiac surface potential.
The correlation coefficients between the simulation results and the clinical data range from 75.76 to 84.61%. The P waves, PR intervals, QRS complex, and T waves in the simulated waveforms were within the normal clinical range, and the distribution trend of the simulated body surface potential mapping was consistent with the clinical data. The coefficient of determination R between the reconstruction results of all the algorithms and the true value is above 0.80, and the mean absolute error is below 2.1 mV, among which the R of long short-term memory network is about 0.99 and the mean absolute error about 0.5 mV.
The electrophysiologic model constructed in this study can reflect cardiac electrical activity, and contains the mapping relationship between the cardiac potential and the body surface potential. In cardiac potential reconstruction, long short-term memory network has significant advantages over other algorithms.
Not applicable.
心律失常的风险分层和预后取决于患者的个体情况，而侵入性诊断方法可能对患者健康有风险，并且当前的非侵入性诊断方法适用于很少的疾病类型，缺乏敏感性和特异性。心脏电生理成像（ECGI）技术能够准确且非侵入性地反映心脏活动，这对心脏病的诊断和治疗具有重要意义。本文旨在通过结合模拟模型和深度学习方法为实现ECGI提供一种新的解决方案。
构建完整的三维双域心脏电生理活动模型，并获取模拟心电图数据作为训练样本。分别使用粒子群优化 - 反向传播神经网络、卷积神经网络和长短期记忆网络来重建心脏表面电位。
模拟结果与临床数据之间的相关系数在75.76%至84.61%之间。模拟波形中的P波、PR间期、QRS波群和T波均在正常临床范围内，模拟体表电位映射的分布趋势与临床数据一致。所有算法的重建结果与真实值之间的决定系数R均高于0.80，平均绝对误差低于2.1 mV，其中长短期记忆网络的R约为0.99，平均绝对误差约为0.5 mV。
本研究构建的电生理模型能够反映心脏电活动，并包含心脏电位与体表电位之间的映射关系。在心脏电位重建方面，长短期记忆网络相对于其他算法具有显著优势。
不适用。
