Abstract
Premature ventricular contractions (PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram (ECG), which is onerous and time-consuming. In this study, deep learning was applied to develop models that can detect PVCs in children automatically. This computer-aided diagnosis model achieved high accuracy while sustained stable performance. It could save time and repeated efforts for physicians, enabling them to focus on more complicated tasks.This study is a first step toward children’s PVC auto-detection in clinics. Further study will improve the model’s performance with optimized structure and more data in different sources, while facing the challenges of the variety and uncertainty of children’s ECG with heart diseases.
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Foundation item: UM-SJTU Joint Institute Start-up Fund (No. 248)
Author contributions: LIU Yixiu, HUANG Yujuan, WANG Jianyi contributed equally to this work.
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Liu, Y., Huang, Y., Wang, J. et al. Detecting Premature Ventricular Contraction in Children with Deep Learning. J. Shanghai Jiaotong Univ. (Sci.) 23, 66–73 (2018). https://doi.org/10.1007/s12204-018-1911-3
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DOI: https://doi.org/10.1007/s12204-018-1911-3