# Research on ECG Signal Classification Based on Hybrid Residual Network
## 1 Introduction
introduced the MobileNetV2 model, which incorporates inverted residuals and linear bottlenecks, enabling efficient feature extraction while significantly decreasing computational costs, achieving an accuracy of 96.7% [28]. This model approach possesses extensive application potential in mobile devices, especially for an ECG monitoring apparatus necessitating real-time processing [9]. Despite the significant potential of hybrid models in achieving a balance between performance and efficiency, their design and optimization processes are intricate procedures, presenting numerous challenges in actual implementations. This research proposes an ECG signal-processing model employing a Hybrid Residual Network (Hybrid ResNet) to overcome the constraints of current methods in managing complicated signal patterns and enhancing computing efficiency in real applications. The Hybrid ResNet effectively combines standard convolution and depthwise separable convolution, enhancing network efficiency and feature extraction skills. Specifically, this paper utilizes standard convolution layers to effectively capture local signal properties while introducing depthwise separable convolutions that distinguish spatial and channel components, therefore substantially enhancing the model's computational efficiency [19; 23]. Furthermore, this paper applies the SMOTE to balance the dataset in conjunction with high-pass Butterworth filtering and discrete wavelet transform for denoising, thereby augmenting the input signal quality and detection ability of minority arrhythmia classes, ultimately enhancing the overall classification performance. Finally, the implementation of cross-layer residual connections prevents the vanishing gradient problem during the training of deep networks [24; 29]. The main contributions of this paper include the following:  
A new Hybrid Residual Network architecture is proposed, integrating standard convolution with depthwise separable convolution. The hybrid residual module is embedded into the existing CNN architecture, enhancing the computing efficiency while preserving accuracy in the analysis of extensive ECG data. The data preprocessing method has been optimized by balancing the dataset using the SMOTE and denoising via discrete wavelet transform. Further, long-term sequential signals are split into individual heartbeats for training, improving the model's classification performance. The effectiveness of each module in the Hybrid Residual Network was validated through ablation experiments. The trials revealed that the combination of depthwise separable convolution with standard convolution significantly improved the computational efficiency, while data balancing and preprocessing steps enhanced the classification accuracy. The comprehensive experimental results confirm the superior performance and practical potential of the proposed network in processing ECG signals. The subsequent sections of this paper are structured as follows: Section 2 describes the datasets and data preprocessing used in this study, along with the proposed model. Section 3 delineates the experimental validation of the model's performance and the subsequent analysis of results. Finally, Section 4 provides the conclusions and directions for future research.