# Research on ECG Signal Classification Based on Hybrid Residual Network
## 1 Introduction  
Lifestyle alterations, occupational stress, an expedited life rhythm, and poor dietary practices are key factors contributing to heart disease, heart failure, and heart attacks [1]. Statistics from the World Health Organization (WHO) indicate that approximately 17.9 million individuals succumb to cardiovascular diseases (CVDs) annually. Consequently, cardiovascular diseases rank among the primary causes of mortality globally [2]. Electrocardiogram (ECG) signals are essential for diagnosing cardiac disorders [3]. ECG signals record the electrical activity of the heart and serve as a crucial clinical tool for detecting arrhythmias, myocardial infarctions, and various cardiac disorders [4]. Electocardiogram (ECG) signals, which are generated by the electrical activity of the heart, are inherently non-linear and nonstationary, reflecting their complex nature. This complexity is evident across multiple dimensions. First, ECG signals exhibit significant multiscale characteristics, incorporating high-frequency components such as QRS complexes, which occur over milliseconds, alongside low-frequency components like P- and T-waves, spanning several seconds. Each heartbeat consists of the P-wave, QRS complex, and T-wave, as shown in Figure 1. Furthermore, ECG signals are influenced by various physiological and pathological factors, such as electrolyte imbalances, myocardial ischemia, and variations in lead placement, resulting in morphological and temporal variability [5]. The complexity is further exacerbated during arrhythmic events, such as atrial fibrillation or ventricular fibrillation, where the signals exhibit heightened non-linearity and randomness [6]. Additionally, ECG signals are susceptible to external noise, including baseline wander, electromyographic interference, and electromagnetic disturbances, presenting additional challenges for effective signal analysis [7]. These intrinsic and extrinsic factors collectively contribute to the ECG's status as a highly complex biomedical signal, demanding sophisticated methods for feature extraction and classification. The aging population and the increasing prevalence of cardiovascular diseases have heightened the demand for ECG monitoring, rendering the efficient and accurate processing and interpretation of substantial ECG signals a prominent research focus and challenge [8]. Traditional ECG signal-processing methods typically rely on manual feature extraction and shallow machine learning models. For instance, methods like support vector machines (SVMs) and k-nearest neighbors (k-NN) have demonstrated effective performances on specific applications. [PERSON] et al. proposed an automatic classification method based on ECG morphology and heartbeat interval features, employing an SVM model that attained a 92.7% accuracy on the MIT-BIH Arrhythmia Database [9]. [PERSON] et al. proposed a k-NN-based classification method that distinguishes between various types of arrhythmias by calculating the Euclidean distance between heartbeats [10]. In addition, statistical processing methods based on random process models, such as autoregressive models and Markov chain models, have also been widely applied to ECG signal analysis. These methods leverage the statistical characteristics of ECG signals to estimate signal dynamics, enabling the detection of anomalies or patterns in heart rhythms. For instance, [PERSON] et al. applied autoregressive modeling to analyze ECG signals with baseline drift, demonstrating its effectiveness in feature extraction and anomaly detection [11]. Similarly, [PERSON] and [PERSON] utilized a hidden Markov model (HMM) to classify arrhythmias by modeling the temporal dependencies of ECG signals, achieving competitive performance on complex datasets [12]. Although these methods perform well in processing ECG signals, they are heavily reliant on manual feature extraction, which is a tedious process requiring specialized knowledge [13,14].