Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.
Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG.
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DOI：
10.1109/tbme.2017.2756869
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
He B
中科院分区：
文献类型：
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作者：
Yang T;Yu L;Jin Q;Wu L;He B
本文提出了一种基于卷积神经网络（CNN）和真实计算机心脏模型的12导联心电图（ECG）定位室性早搏起源的新方法。该方法由两个CNN （Segment CNN和Epi-Endo CNN）组成，对来自25个节段的心室源和来自心外膜（Epi）或心内膜（Endo）的心室源进行分类。输入分别为12导联心电图全时程和QRS复合体前半部分。在将心室计算机模型注册到单个患者的心脏后，通过将不同位置的单次起搏产生的心室电流偶极子与患者特定的导联场相乘来生成训练数据集。通过计算cnn返回的分类加权重心来定位PVC的起源。在各种噪声水平和心脏配准误差下，进行了大量的计算机模拟来评估所提出的方法。此外，该方法对9例人类室性早搏患者的90次心室搏动进行了评估，并与消融结果进行了比较。计算机模拟评估对Segment CNN（~78%）和Epi-Endo CNN（~90%）返回了相对较高的精度。在9例PVC患者的临床试验中，平均定位误差为11 mm。我们的模拟和临床评估结果证明了基于cnn的PVC定位方法的能力和优点。本研究提出了一种利用CNN仅利用12导联心电图定位心律失常源头的新方法，可能在未来实时监测和定位心律失常源头指导消融治疗中具有重要应用价值。
This paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model. The proposed method consists of two CNNs (Segment CNN and Epi-Endo CNN) to classify among ventricular sources from 25 segments and from epicardium (Epi) or endocardium (Endo). The inputs are the full time courses and the first half of QRS complexes of 12-lead ECG, respectively. After registering the ventricle computer model with an individual patient’s heart, the training datasets were generated by multiplying ventricular current dipoles derived from single pacing at various locations with patient-specific lead field. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. A number of computer simulations were conducted to evaluate the proposed method under a variety of noise levels and heart registration errors. Furthermore, the proposed method was evaluated on 90 PVC beats from 9 human patients with PVCs and compared against ablation outcome in the same patients. The computer simulation evaluation returned relatively high accuracies for Segment CNN (~78%) and Epi-Endo CNN (~90%). Clinical testing in 9 PVC patients resulted an averaged localization error of 11 mm. Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN-based method for localization of PVC. This work suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.