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Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system.

Authors
  • Zhang, Xin1, 2, 3, 4
  • Gu, Kai5
  • Miao, Shumei1, 2, 3
  • Zhang, Xiaoliang1, 2, 3
  • Yin, Yuechuchu1, 2, 3
  • Wan, Cheng2, 3
  • Yu, Yun2, 3
  • Hu, Jie2, 3
  • Wang, Zhongmin1, 2, 3
  • Shan, Tao1, 2, 3
  • Jing, Shenqi1, 2, 3
  • Wang, Wenming1, 2, 3
  • Ge, Yun4
  • Chen, Yin4
  • Guo, Jianjun1, 2, 3
  • Liu, Yun1, 2, 3
  • 1 Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China. , (China)
  • 2 Department of Information, The First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China. , (China)
  • 3 Institute of Medical Informatics and Management, Nanjing Medical University, Nanjing 210029, China. , (China)
  • 4 School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China. , (China)
  • 5 Division of Cardiology, The First Affiliated Hospital, Nanjing Medical University, Nanjing 210029, China. , (China)
Type
Published Article
Journal
Cardiovascular diagnosis and therapy
Publication Date
Apr 01, 2020
Volume
10
Issue
2
Pages
227–235
Identifiers
DOI: 10.21037/cdt.2019.12.10
PMID: 32420103
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Automated electrocardiogram (ECG) diagnosis could be a useful aid for clinical use. We applied a deep learning method to build a system for automated detection and classification of ECG signals. We first trained a convolutional neural network (CNN) to detect cardiovascular disease in ECG signals using a training data set of 259,789 ECG signals collected from the cardiac function rooms of a tertiary care hospital. The CNN classification was validated using an independent test data set of 18,018 ECG signals. The labels used covered >90% of clinical diagnoses. The system grouped ECGs into 18 classifications-17 different types of abnormalities and normal ECG. The overall accuracy of the model was tested and found to be close to 95%; the accuracy for diagnosis of normal rhythm/atrial fibrillation was 99.15%. The proposed CNN model could help reduce misdiagnosis and missed diagnosis in primary care settings and also improve efficiency and save manpower cost for large general hospitals. 2020 Cardiovascular Diagnosis and Therapy. All rights reserved.

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