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

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


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.