@article{CDT34803,
author = {Xin Zhang and Kai Gu and Shumei Miao and Xiaoliang Zhang and Yuechuchu Yin and Cheng Wan and Yun Yu and Jie Hu and Zhongmin Wang and Tao Shan and Shenqi Jing and Wenming Wang and Yun Ge and Yin Chen and Jianjun Guo and Yun Liu},
title = {Automated detection of cardiovascular disease by electrocardiogram signal analysis: a deep learning system},
journal = {Cardiovascular Diagnosis and Therapy},
volume = {10},
number = {2},
year = {2020},
keywords = {},
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.},
issn = {2223-3660}, url = {https://cdt.amegroups.org/article/view/34803}
}