# Research on ECG Signal Classification Based on Hybrid Residual Network
## 3 Experimental Results and Discussion
### Comparison with Other State-of-the-Art Methods  
This section compares the proposed Hybrid Residual Network (Hybrid ResNet) model with several state-of-the-art ECG classification methods (refer to Table 4), each possessing distinct traits and advantages. This comparison aims to validate the effectiveness of our proposed strategy against other advanced methods. [PERSON] et al. [30] proposed a methodology that uses stationary wavelet transform (SWT) combined with a deep convolutional neural network (DCNN). SWT can effectively separate high-frequency signals, enhancing feature extraction capabilities, while DCNN identifies intricate temporal patterns. [PERSON] et al. [31] utilized RR interval segmentation in conjunction with a bidirectional long short-term memory network (BiLSTM). This combination allows the model to discern temporal dependencies between consecutive heartbeats, proving highly effective for detecting rhythm-related abnormalities. [PERSON] et al. [32] employed discrete wavelet transform (DWT) and a support vector machine (SVM). DWT is particularly suitable for feature extraction from nonstationary signals like ECG, while SVM delivers robust classification performance. [PERSON] et al. [33] introduced a pattern-adaptive hybrid classifier that amalgamates multiple classifiers to adapt to diverse heart rhythm patterns, thus improving the recognition performance for minority class samples. [PERSON] et al. [34] used DWT combined with independent component analysis (ICA) and a multilayer perceptron (MLP) neural network for classification. The integration of DWT and ICA facilitates signal denoising, consequently augmenting signal clarity and raising classification efficacy. [PERSON] et al. [35] formulated a model utilizing an adversarial autoencoder to enhance the model's robustness to noise. The autoencoder helps to learn latent representations that are robust to variations, thus improving the classification accuracy. Figure 4: A plot illustrating the training and evaluation accuracies on the MIT-BIH dataset as a function of the number of epochs. Figure 5: Classifier performance using confusion matrix with normalization. We conducted an extensive comparison analysis using the MIT-BIH Arrhythmia Database and the previously indicated advanced methodologies. The experimental results show that our proposed Hybrid ResNet model surpasses all other methods across multiple metrics, including accuracy, sensitivity, specificity, positive predictive value (PPV), and F1-score. Specifically, our model achieved an overall classification accuracy of 99.09%, surpassing all other assessed approaches. Compared to the method based on SWT and deep convolutional neural networks (DCNNs) proposed by [PERSON] et al., our model improves the classification accuracy by approximately 0.46%. However, while the sensitivity of the proposed method is slightly lower than those of [PERSON] et al. and [PERSON] et al., these observations can be attributed to specific trade-offs in preprocessing and model design. [PERSON] et al. employed SWT to enhance high-frequency feature extraction, which is particularly beneficial for detecting subtle rhythm variations. Additionally, compared to the model employing RR interval segmentation and a BiLSTM network proposed by [PERSON] et al., the Hybrid ResNet exhibits substantial enhancements in both sensitivity and specificity, attributable to its superior feature extraction capabilities and better preprocessing strategies, particularly showcasing greater robustness in identifying minority arrhythmia classes. However, [PERSON] et al. utilized BiLSTM, which excels in capturing temporal dependencies. Moreover, relative to the DWT- and SVM-based model proposed by [PERSON] et al., our model exhibits higher accuracy and sensitivity, underscoring the Hybrid ResNet's adaptability and robustness in managing intricate signal patterns. However, the PPV is marginally lower than that of [PERSON] et al. [PERSON] et al.