Abstract
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.
Highlights
Introduction conditions of the Creative CommonsThe dangers of surgery and anesthesia have been issues of concern for both physicians and patients for a long time
This study indicated that with the employment of an Arduino Uno, Raspberry Pi, AD8232 heart rate monitor, and deep neural network, it is possible to carry out the realtime remote monitoring of variation in patients’ ECGs
Such a prototype may not replace the current laboratory or hospital equipment, but the Internet of Things (IoT) concept we proposed is aimed at enabling doctors to discuss and analyze the same patient’s condition in real time
Summary
The dangers of surgery and anesthesia have been issues of concern for both physicians and patients for a long time. In addition to the operation itself, there are concerns about the pain of the operation, ignorance of the anesthesiologist, and the method of anesthesia [1]. It is essential for surgical patients or their families to understand the issues related to surgery and anesthesia. While we need to engage in a pre-anesthesia assessment, there are many different methods we can choose, including the following: electrocardiogram (ECG), ultrasound (US), a blood test, chest X-ray, and history taking in the clinic [2,3,4]. Except for the methods mentioned above, the last several decades of research have given us helpful information on automated or artificial intelligence methods to assist the pre-anesthesia assessment
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