# Research on ECG Signal Classification Based on Hybrid Residual Network
## 3 Experimental Results and Discussion
### Ablation Studies  
To validate the effectiveness of the proposed algorithm, we conducted multiple ablation experiments, progressively incorporating the traditional CNN network, ECG signal preprocessing, ResNet, and Hybrid ResNet architecture. The preprocessing procedures encompassed the SMOTE, discrete wavelet transform denoising, and a high-pass filter. All experiments were performed using the MIT-BIH Arrhythmia Database. Table 5 presents a comparison of the alterations in classification performance following the incremental addition of various components to the traditional CNN model, aimed at assessing the impact of the proposed algorithm on classification performance. The experimental results reveal that the improved preprocessing method augmented the classification accuracy by 0.48%. On this basis, the incorporation of the ResNet model further improved the accuracy by 0.26%. Finally, replacing the traditional ResNet with the Hybrid ResNet increased the accuracy to 99.09%, representing a significant advancement over the baseline model. This indicates that the proposed improved model can effectively extract temporal features, thus enhancing classification performance. The ablation experiments (refer to Table 5) demonstrate that data preprocessing plays a crucial role in improving model performance. This paper utilized a high-pass Butterworth filter and discrete wavelet transform to denoise and smooth the raw ECG signals, successfully eliminating baseline drift and high-frequency noise. These preprocessing operations enhanced the signal characteristics, markedly elevating the quality of the input for the classification model. The inclusion of preprocessing steps increased the classification accuracy from 98.24% to 98.72%. In particular, the recognition rates of minority class signals, such as supraventricular arrhythmias and fusion beats, were improved. This provided more distinct features for successive deep learning models, enabling the classifier to train more effectively. Building on the preprocessing steps, this paper developed the ResNet architecture to augment the model's feature extraction capabilities. The incorporation of residual connections addresses the vanishing gradient problem, enabling the training of deeper networks. By maintaining a stable gradient flow, the network is able to extract hierarchical features from ECG signals, such as the positions and shapes of R-wave peaks and the morphological patterns of QRS complexes, while also capturing the rhythm variations between heartbeats. This deeper and more robust feature extraction improves the model's ability to distinguish between normal and abnormal signals, particularly for minority classes like supraventricular ectopic beats (SVEBs). The ablation experiments indicated that the incorporation of the ResNet module improved the model's accuracy from 98.72% to 98.98%, with notable gains in sensitivity and specificity. These results highlight the ability of the ResNet architecture to generalize effectively across complex ECG signals, providing superior classification performance. The ablation experiments highlight the effectiveness of the Hybrid ResNet design, which integrates residual connections with depthwise separable convolutions. Depthwise separable convolutions significantly reduce the number of parameters and computational complexity by decomposing standard convolutions into two smaller operations: spatial convolutions and channel-wise convolutions. This enables the model to focus more efficiently on local signal patterns, such as R-wave morphology, while preserving critical channel-specific features across multiple layers. Combined with residual connections, the Hybrid ResNet achieves both computational efficiency and improved feature extraction capabilities. Compared to the traditional ResNet, the Hybrid ResNet improved classification accuracy from 98.98% to 99.09%, with additional gains in sensitivity and specificity, showcasing the synergistic benefits of the hybrid architecture.