Abstract
Reconstruction of a 12-lead electrocardiogram (ECG) from a serial 3-lead ECG has been researched in the past to satisfy the need for more wearing comfort and ambulatory situations. The accuracy and real-time performance of traditional methods need to be improved. In this study, we present a novel method based on convolutional neural networks (CNNs) for the synthesis of missing precordial leads. The results show that the proposed method receives better similarity and consumes less time using the PTB database. Particularly, the presented method shows outstanding performance in reconstructing the pathological ECG signal, which is crucial for cardiac diagnosis. Our CNN-based method is shown to be more accurate and time-saving for deployment in non-hospital situations to synthesize a standard 12-lead ECG from a reduced lead-set ECG recording. This is promising for real cardiac care.
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18 June 2019
The financial support information in the original article is incorrect. The correct information is given below:
Project supported by the National Natural Science Foundation of China (No. 61702044), the Fundamental Research Funds for the Central Universities, China (No. 2017RC27), and the BUPT Excellent Ph.D. Students Foundation.
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Project supported by the National Natural Science Foundation of China (No. 6170204), the Fundamental Research Funds for the Central Universities, China (No. 2017RC27), and the BUPT Excellent Ph.D. Students Foundation
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Wang, Ld., Zhou, W., Xing, Y. et al. A novel method based on convolutional neural networks for deriving standard 12-lead ECG from serial 3-lead ECG. Frontiers Inf Technol Electronic Eng 20, 405–413 (2019). https://doi.org/10.1631/FITEE.1700413
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DOI: https://doi.org/10.1631/FITEE.1700413