Abstract
Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.
Highlights
Monitori machine learning methods that require manual feature extraction, and the other includes sensors with smaller volumes and higher sensitivity may be based on fiber optic techn end-to-end deep learning methods
Many models of deep learning have been applied to the classified diagnosis of ECG, and these studies have improved the performance of ECG diagnosis to a new level
We introduced in detail the real-time detection algorithm used for ECG monitoring equipment, mainly the deep learning methods of heart rhythm classification and myocardial ischemia (MI) detection in ECG
Summary
Ischemic heart disease (IHD) causes over 8 million deaths globally every year [1], making it the leading cause of death. It contains different types of arrhythmias including atrial fibrillation, ventricular tachycardia, and in severe cases, myocardial ischemia (MI). With the development of computer technology, computer-aided diagnosis technology is widely used in the medical field [2,3,4], which can liberate valuable medical resources. These methods in the direction of ECG diagnosis include thresholdbased methods, machine learning methods, and recently rapidly developed deep learning methods
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