# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 1 Introduction  
According to a World Health Organization study in November 2021 [1], 31% of fatalities worldwide in 2019 were attributable to cardiovascular diseases. Cardiovascular diseases remain one of the largest causes of death worldwide [2]. Analyzing heart activity through an electrocardiogram is presently the most prevalent way to find cardiac disease [3; 4]. Electrocardiography is a method that records the electrical activity produced by the heart from the biological body surface during each beating cycle using an electrocardiographic monitoring equipment [5; 6]. ECG signals represent critical information about the vital signs of a person and give information about the health of the heart and cardiovascular system. Long-term ECG signal measurement is becoming more affordable and practical as a result of the growing popularity of wearable medical diagnostic equipment. A typical length of time for ambulatory ECG examinations nowadays is 24 h, 48 h, or 72 h [7; 8; 9]. Medical professionals must go through and choose the best slices from this lengthy ECG data to analyze the heart activity state. A considerable number of medical professionals must be sent for this time-consuming endeavor, especially in isolated places with limited access to medical care. Additionally, compared to the ECG signals picked up by clinical equipment, the signals acquired by wearable health testing devices are more susceptible to different interferences (such as power line interference, baseline drift, impulse noise, and muscle noise) [10]. Therefore, the development of ECG signal quality evaluation methods for mobile medical health detection devices continues to be a challenging problem. Machine learning is a branch of artificial intelligence and a necessary way to achieve artificial intelligence [11]. Its purpose is to study how computers can simulate or implement human learning behavior to acquire new knowledge or skills. Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use the patterns to make predictions about unknown data [12]. Traditional machine learning methods work in three main steps: data preprocessing, feature extraction, and signal classification. These signal processing steps are often used in ECG signal quality assessment tasks [13]. For example, the methods of ECG signal quality assessment based on the machine learning principle include analysis based on the autoregressive model [14], analysis based on morphological characteristics [15], and a combination of multiple signal quality indicators and support vector machines algorithm [16].The manual calculation of features is a time-consuming step that is unsuitable for handling large amounts of data, and all of the methods mentioned above use some custom thresholds as criteria for signal quality assessment. These criteria are subjective and may contain insufficient or redundant information. Deep learning is a branch of machine learning; it is an algorithm based on representation learning of data in machine learning [17; 18; 19]. Different from traditional machine learning methods, deep learning models do not need to design features manually but use deep neural networks to form more abstract high-level representations (or features) by combining low-level features [17], so that the model can understand data more deeply. It has been applied to various fields such as computer vision, natural language processing, and bioinformatics, and has achieved wonderful results [20; 21; 22]. There are also some ECG signal analysis works on the deep learning network [23; 24; 25]. However, with the increase of these network layers, the model performance will be degraded. As a kind of deep learning network model, residual network can be used to solve the problem of network performance degradation [26]. Therefore, in this paper, we use the residual network to complete the ECG signal quality evaluation.