Key Laboratory of Coal Mine Health and Safety of Hebei Province, School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian New Town, Tangshan 063210, China.
Jining Center for Disease Control and Prevention, No. 26 Yingcui Road, Rencheng District, Jining 272000, China.
Int J Environ Res Public Health. 2022 Dec 20;20(1):9. doi: 10.3390/ijerph20010009.
To process and extract electrocardiogram (ECG, ECG, or EKG) features using a convolutional neural network (CNN) to establish an ECG-assisted diagnosis model.
Coal workers who underwent physical examinations at Gequan Mine Hospital and Dongpang Mine Hospital of Hebei Jizhong Energy from July 2020 to September 2020 were selected as the study subjects. The ECG images were preprocessed. We use Python software and convolutional neural network to establish ECG images recognition and classification model.We usecalibration curve, calibration-in-the-large, Brier score, specificity, sensitivity, F1 score, Kappa value, accuracy, and area under the curve (AUC) of ROC to evaluate the performance of the model.
The number of abnormal ECG results was 849, and the rate of abnormal results was 25.02%. The test set accuracies of the sinus bradycardia model, nonspecific intraventricular conduction delay model, myocardial ischemia model, and sinus tachycardia model were 97.66%, 96.49%, 93.62%, and 93.02%, respectively; sensitivities were 96.63%, 96.30%, 96.88% and 95.24%, respectively; specificities were 98.78%, 96.67%, 86.67%, and 90.90%, respectively; Brier scores were 0.03, 0.07, 0.09, and 0.11, respectively; Calibration-in-the-large values were 0.026, 0.110, 0.041, and 0.098, respectively.
The convolutional neural network model can accurately identify the main ECG abnormality types of coal workers. Additionally, the main ECG abnormalities in these coal company workers were sinus bradycardia, non-specific intraventricular conduction delay, myocardial ischemia, and sinus tachycardia.
使用卷积神经网络（CNN）处理和提取心电图（ECG、ECG 或 EKG）特征，建立心电图辅助诊断模型。
选取 2020 年 7 月至 2020 年 9 月在河北冀中能源葛泉矿医院和东庞矿医院体检的煤工作为研究对象，对心电图图像进行预处理。采用 Python 软件和卷积神经网络建立心电图图像识别和分类模型。采用校准曲线、大校准、Brier 评分、特异性、敏感性、F1 评分、Kappa 值、准确性和 ROC 曲线下面积（AUC）评估模型性能。
异常心电图结果 849 例，异常结果率为 25.02%。窦性心动过缓模型、非特异性室内传导延迟模型、心肌缺血模型和窦性心动过速模型的测试集准确率分别为 97.66%、96.49%、93.62%和 93.02%；灵敏度分别为 96.63%、96.30%、96.88%和 95.24%；特异性分别为 98.78%、96.67%、86.67%和 90.90%；Brier 评分分别为 0.03、0.07、0.09 和 0.11；大校准值分别为 0.026、0.110、0.041 和 0.098。
卷积神经网络模型能准确识别煤工主要心电图异常类型，该煤炭企业工人的主要心电图异常类型为窦性心动过缓、非特异性室内传导延迟、心肌缺血和窦性心动过速。
