Abstract
Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%.
Highlights
We propose an ECG signal classification method based on the combination of Gramian angular fields (GAFs) and an improved deep residual network (ResNet) to provide an accurate and rapid diagnosis for the sensors
An artificial intelligence-enabled ECG algorithm based on improved ResNet for a wearable ECG device is proposed in this paper
If popular science application software for ECG monitoring wants to realize the diagnosis of ECG signals, it must be realized by calling the MatLab algorithm with a Python background, which may be limited by the network
Summary
With the development of society, individuals are focusing more on maintaining their health. A growing number of people are relying on timely and effective technical approaches to safeguard their health and safety [1,2]. Currently available medical monitoring devices exhibit certain advantages in terms of professionalism and accuracy, their monitoring time and scenarios are subject to certain restrictions. The ECG signals from the human body are relatively weak, low-frequency signals, which places a higher requirement for hardware acquisition equipment to obtain real-time and accurate
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