EPMoghaddam Dorsa, Banta Anton, Post Allison, Razavi Mehdi, Aazhang Behnaam
Department of Electrical and Computer Engineering, Rice University, Houston, United States of America.
Electrophysiology Clinical Research and Innovations, Texas Heart Institute, Houston, United States of America.
Conf Rec Asilomar Conf Signals Syst Comput. 2023 Oct-Nov;2023:1054-1058. doi: 10.1109/ieeeconf59524.2023.10476822.
This paper presents a novel approach to synthesize a standard 12-lead electrocardiogram (ECG) from any three independent ECG leads using a patient-specific encoder-decoder convolutional neural network. The objective is to decrease the number of recording locations required to obtain the same information as a 12-lead ECG, thereby enhancing patients' comfort during the recording process. We evaluate the proposed algorithm on a dataset comprising fifteen patients, as well as a randomly selected cohort of patients from the PTB diagnostic database. To evaluate the precision of the reconstructed ECG signals, we present two metrics: the correlation coefficient and root mean square error. Our proposed method achieves superior performance compared to most existing synthesis techniques, with an average correlation coefficient of 0.976 and 0.97 for datasets, respectively. These results demonstrate the potential of our approach to improve the efficiency and comfort of ECG recording for patients, while maintaining high diagnostic accuracy.
本文提出了一种新颖的方法，即使用患者特异性编码器-解码器卷积神经网络，从任意三个独立的心电图（ECG）导联合成标准的12导联心电图。目的是减少获取与12导联心电图相同信息所需的记录位置数量，从而提高记录过程中患者的舒适度。我们在一个包含15名患者的数据集以及从PTB诊断数据库中随机选择的一组患者中评估了所提出的算法。为了评估重建ECG信号的精度，我们提出了两个指标：相关系数和均方根误差。与大多数现有的合成技术相比，我们提出的方法具有卓越的性能，两个数据集的平均相关系数分别为0.976和0.97。这些结果证明了我们的方法在提高患者心电图记录效率和舒适度的同时保持高诊断准确性的潜力。
