Why the study?
Reconstructing a 12-lead ECG from a serial 3-lead ECG improves wearing comfort in ambulatory situations, but the accuracy and real-time performance of traditional methods need improvement.
Does a CNN-based method improve the accuracy and speed of deriving standard 12-lead ECGs from serial 3-lead ECGs compared to traditional methods?
Does a CNN-based method improve the accuracy and speed of deriving standard 12-lead ECGs from serial 3-lead ECGs compared to traditional methods?
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A novel CNN-based method accurately and rapidly synthesizes standard 12-lead ECGs from 3-lead recordings, showing promise for ambulatory cardiac monitoring.
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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Wang et al. (2019) studied this question.
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