# Research on ECG Signal Classification Based on Hybrid Residual Network
## 1 Introduction
While classical approaches offer certain advantages in specific applications, their limitations significantly constrain their effectiveness in handling complex ECG signals. The application of deep learning technologies in ECG signal processing has gained significant traction in recent years. Convolutional neural networks (CNNs) have emerged as a mainstream method due to their powerful feature extraction capabilities. [PERSON] et al. introduced a real-time, patient-specific ECG classification method based on a one-dimensional convolutional neural network, achieving 95.9% accuracy on the MIT-BIH Arrhythmia Database [15]. Additionally, [PERSON] et al. developed a deep neural network that processes long-term ECG records, attaining an arrhythmia diagnosis ability equivalent to that of cardiologists, with an accuracy of 97.5% [16]. However, standard CNNs frequently face challenges, including disappearing gradients and a high computational cost when managing deep networks [17,18]. To address these problems, researchers have implemented  
Figure 1: ECG waveform with key intervals and segments. approaches such as batch normalization and residual connections to enhance training efficiency and stability [19; 20]. Despite the superior classification performance of deep learning methods, their computational resource demands and model complexity in practical applications still require further optimization. To enhance ECG classification performance, various preprocessing strategies have been extensively explored in recent years. Prevalent techniques encompass wavelet transform, Fourier transform, and empirical mode decomposition (EMD). For example, [PERSON] and [PERSON] performed a detailed study on the application of wavelet transform in ECG signal processing, effectively extracting features via multiscale analysis and achieving a classification accuracy of 93.4% [21]. [PERSON] et al. employed the empirical wavelet transform (EWT) for ECG signal denoising, achieving a signal-to-noise ratio (SNR) improvement of 27.63 dB and a root mean square error (RMSE) of 0.0007, outperforming traditional methods and effectively preserving ECG signal integrity [22]. Additionally, [PERSON] et al. proposed the SMOTE, which rectifies imbalanced datasets by generating new synthetic samples and has demonstrated efficacy in various signal-processing tasks [23]. Nonetheless, each of these strategies possesses distinct limits, rendering a single method often inadequate for handling complex signal patterns. In recent years, there has been a rise in research that integrates multiple preprocessing methods. For instance, the combination of wavelet transform and the SMOTE has significantly improved classifier performance, with an accuracy of 94.2% [24]. However, current preprocessing efforts exhibit significant shortcomings in optimizing the combination of denoising methods and signal feature augmentation techniques, making them inadequate to fully tackle the complexity and diversity of ECG signals. This poses obstacles and opportunities for enhancing categorization performance. Hybrid models have recently garnered significant attention. These models combine the advantages of different network architectures to improve classification efficacy. [PERSON] et al. proposed a hybrid network based on a CNN and long short-term memory (LSTM) for the classification of ECG signals, markedly enhancing classification accuracy and robustness, achieving an accuracy of 97.2% [25]. LSTM networks are proficient in capturing long-term dependencies in time-series data, making them particularly appropriate for handling ECG signals that display temporal dependencies [26]. Similarly, [PERSON] et al. introduced a hybrid CNN-Transformer model, ECG-TransCovNet, which leverages CNNs for localized feature extraction while employing the Transformer's self-attention mechanism to model long-range dependencies and complex relationships, effectively capturing both spatial and temporal features from large-scale datasets, achieving an accuracy of 97.5% [27]. [PERSON] et al.