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Automated Detection of Cardiovascular Disease by Electrocardiogram Signal Analysis: a Deep Learning System by Zhang, Xin; Gu, Kai; Miao, Shumei; Zhang, Xiaoliang; Yin, Yuechuchu; Wan, Cheng; Yu, Yun; Hu, Jie; Wang, Zhongmin; Shan, Tao; Jing, Shenqi; Wang, Wenming; Ge, Yun; Chen, Yin; Guo, Jianjun; Liu, Yun is a Medicine article available to read on EtoBox.

What is Automated Detection of Cardiovascular Disease by Electrocardiogram Signal Analysis: a Deep Learning System about?

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.

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Author

Zhang, Xin; Gu, Kai; Miao, Shumei; Zhang, Xiaoliang; Yin, Yuechuchu; Wan, Cheng; Yu, Yun; Hu, Jie; Wang, Zhongmin; Shan, Tao; Jing, Shenqi; Wang, Wenming; Ge, Yun; Chen, Yin; Guo, Jianjun; Liu, Yun

Publisher

AME Publishing Company (ISSN 2223-3652)

Published

2020

Language

EN

Field

Medicine (Health Sciences)