# ECG-Signal Multi-Classification Model Based on Squeeze-and-Excitation Residual Neural Networks
## 1 Introduction  
The human heart has an electric transmission system that involuntarily generates regular electrical signals and transmits these signals to the entire heart. Heart disease takes the lives of many people all over the world [1; 2; 3]. Arrhythmia is a condition of irregular heartbeats caused by changes or dysfunctions of this electric transmission system. Arrhythmia can generally be diagnosed in the hospital using a measured electrocardiogram (ECG), which is a record of the electrical activity in the heart, obtained through electrodes located on the skin of the chest and limbs. In recent years, wearable devices such as the Apple Watch [4] and the Zio patch cardiac monitor [5; 6] were introduced to the market; they measure ECG in a new way, different from the conventional device used in hospitals, and they detect irregular heartbeats. The demand for ECG measurement through tele-medical devices is exploding. We are also interested in the development of the tele-medical device for ECG, and for this we needed to develop a deep learning-based algorithm for ECG classification, so in this paper, we proposed the deep learning model for this problem. An ECG usually refers to a 12-lead ECG, which gathers 12 different types of information from the heart. To precisely classify a 12-lead ECG signal, doctors examine the ECG data and diagnose specific arrhythmias based on their medical knowledge and extensive experience. Unfortunately, judgment errors are likely to occur during this process. Even an experienced specialist requires considerable time to analyze the signals, and the accuracy may not be high [5,6]. In addition, in the case of a Holter monitor, the cardiologist cannot see the entire signal, which is usually recorded over several days. Thus, many researchers have attempted to classify 12-lead ECG signals automatically and accurately. Thus far, rule-based algorithms that classify ECG by rules such as comparison of the length of a specific section (for example, based on the QRS duration or the RR interval etc.) in the entire ECG, or whether the beats of the ECG are regular or not, have been unsuitable for use in practice, owing to their poor performance. In addition, this classification has been approached using various machine-learning methods, e.g., logistic regression [7], support vector machines (SVMs) [8], random forests [9], and K-nearest neighbors [10,11]. In recent years, deep-learning based approaches are showing better performance in ECG signal classification than rule-based algorithms and machine learning algorithms. The most obvious approach for deep-learning research using ECG data involves creating a deep-learning model using all 12-lead ECG data measured in a hospital. [PERSON] et al. [12] found that the accuracy of a deep-learning network (with 13 convolutional layers and 3 fully connected layers) using all 12-lead ECG data was higher than that of a rule-based algorithm. However, in most cases, rather than using all the ECG information, researchers have approached ECG signal classification using the information from one specific lead; e.g., lead I or lead II. [PERSON] et al. [13] used a residual network (ResNet) with six residual blocks and an Alex network to classify atrial fibrillation (Normal/Atrial Fibrillation), which provided accuracies of 99.9 and 99.7%, respectively. [PERSON] et al. [5] and [PERSON] et al. [6] showed that a deep-learning model, using a 34-layer ResNet model, exceeded the performance of average cardiologists in terms of ECG discrimination ability of 12 output rhythm classes (10 arrhythmias/Normal/Noise). It is important to note that the data they collected were large-scale, obtained from patients in actual hospitals. Their model used 91,232 modified lead-II ECG records from 53,549 patients, and was recorded using Zio cardiac monitors. Recently, beyond simple convolutional neural network (CNN) structures, attempts have been made to find a better ECG signal-classification structure by using structures that produce good results for image classification. [PERSON] et al.