Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks
Li-Ren Yeh,
Wei-Chin Chen,
Hua-Yan Chan,
Nan-Han Lu,
Chi-Yuan Wang,
Wen-Hung Twan,
Wei-Chang Du,
Yung-Hui Huang,
Shih-Yen Hsu,
Tai-Been Chen
Affiliations
Li-Ren Yeh
Department of Anesthesiology, E-DA Cancer Hospital, I-Shou University, No. 65, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Wei-Chin Chen
Department of Anesthesiology, E-DA Hospital, I-Shou University, No. 1, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Hua-Yan Chan
Department of Medical Radiology, E-DA Cancer Hospital, I-Shou University, No. 1, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Nan-Han Lu
Department of Pharmacy, Tajen University, No. 20, Weixin Road, Yanpu Township, Pingtung County 90741, Taiwan
Chi-Yuan Wang
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Wen-Hung Twan
Department of Life Sciences, National Taitung University, No. 369, Sec. 2, University Road, Taitung 95092, Taiwan
Wei-Chang Du
Department of Information Engineering, I-Shou University, No. 1, Sec. 1, Syuecheng Road, Dashu District, Kaohsiung City 84001, Taiwan
Yung-Hui Huang
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Shih-Yen Hsu
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Tai-Been Chen
Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-su Village, Yan-chao District, Kaohsiung City 82445, Taiwan
Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.
