Department of BioMedical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan.
Electrophysiology and Heart Modelling Institute (IHU-LIRYC), Fondation Bordeaux Université, 33000 Bordeaux, France.
Sensors (Basel). 2022 Mar 17;22(6):2331. doi: 10.3390/s22062331.
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs' ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.
心电图成像是利用体表记录的电位来重建心脏表面的电活动。这被称为心电图的反问题。本研究旨在利用机器学习和深度学习框架改进当前的解决方案方法。同时从猪的心室和体表记录心电图。使用全连接神经网络（FCN）、长短期记忆（LSTM）、卷积神经网络（CNN）方法构建模型。开发了一种在不同猪之间对齐数据的方法。我们使用留一法交叉验证来评估该方法。对于最佳结果，预测 ECG 波的相关系数的总体中位数为 0.74。这项研究表明，神经网络可以用于解决具有相对较小数据集的 ECGi 反问题，其准确性与当前标准方法兼容。
