In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction between the demand for standard 12‑lead ECG information and the limited number of leads collected by portable devices. This study introduces a composite ECG vector reconstruction network architecture based on convolutional neural network (CNN) combined with recurrent neural network by using leads I, II, and V2. This network is designed to reconstruct three‑lead ECG signals into 12‑lead ECG signals. A 1D CNN abstracts and extracts features from the spatial domain of the ECG signals, and a bidirectional long short-term memory network analyzes the temporal trends in the signals. Then, the ECG signals are inputted into the model in a multilead, single-channel manner. Under inter-patient conditions, the mean reconstructed Root mean squared error (RMSE) for precordial leads V1, V3, V4, V5, and V6 were 28.7, 17.3, 24.2, 36.5, and 25.5 μV, respectively. The mean overall RMSE and reconstructed Correlation coefficient (CC) were 26.44 μV and 0.9562, respectively. This paper presents a solution and innovative approach for recovering 12‑lead ECG information when only three‑lead information is available. After supplementing with comprehensive leads, we can analyze the cardiac health status more comprehensively across 12 dimensions.

中文翻译：

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使用深度学习从三导联信号重建 12 导联心电图的可行性和有效性

在移动健康领域，出于便携性和电池寿命等考虑，便携式动态心电图（ECG）监测设备的导联电极数量往往有限。这种情况导致了对标准 12 导联心电图信息的需求与便携式设备收集的有限导联数量之间的矛盾。本研究介绍了一种基于卷积神经网络（CNN）与循环神经网络相结合的复合心电图矢量重建网络架构，使用导联 I、II 和 V2。该网络旨在将三导联 ECG 信号重建为 12 导联 ECG 信号。一维 CNN 从心电图信号的空间域中抽象和提取特征，双向长短期记忆网络分析信号的时间趋势。然后，心电信号以多导联、单通道的方式输入到模型中。在患者间条件下，心前导联 V1、V3、V4、V5 和 V6 的平均重建均方根误差 (RMSE) 分别为 28.7、17.3、24.2、36.5 和 25.5 μV。平均总体 RMSE 和重建相关系数 (CC) 分别为 26.44 μV 和 0.9562。本文提出了一种在只有三导联信息可用时恢复 12 导联心电图信息的解决方案和创新方法。补充综合导联后，我们可以从12个维度更全面地分析心脏健康状况。