# Research on ECG Signal Classification Based on Hybrid Residual Network
## 3 Experimental Results and Discussion  
### Experimental Results  
We employed the Python programming language and a GPU-based TensorFlow backend to assess the suggested method, conducting all experimental data processing on a tower workstation featuring an Intel Xeon (R) Gold 6248R CPU @ 3.00 GHz and an NVIDIA GeForce RTX 3090 (NVIDIA, Santa Clara, California). The proposed network was trained for 50 epochs, with each epoch lasting 68 s. Sparse categorical cross-entropy served as the loss function, Adam functioned as the optimization algorithm, and 113,125 data samples from 5 different categories were used. In this experiment, we used five main evaluation metrics to assess the classification performance of the model: accuracy (ACC), sensitivity (SEN), specificity (SPAC), positive predictive value (PPV), and F1-score. Through the calculation and analysis of these metrics, we can comprehensively evaluate the model's classification ability, especially in the context of imbalanced data. Table 3 presents the evaluation metrics calculated for the proposed model, including the accuracy, sensitivity, specificity, positive predictive value, and F1-score for all five heartbeat categories. Figure 4 shows the classifier accuracy curve throughout the training and evaluation phases relative to the number of epochs. Figure 5 provides the performance of the proposed model using a normalized confusion matrix encompassing all five categories. The diagonal elements reflect successfully classified categories, whereas the off-diagonal elements denote misclassifications. The average of the diagonal values in the normalized confusion matrix provides the overall classification accuracy of the system. Compared to alternative methods in this domain, the proposed method improved the accuracy of heartbeat signal classification while diminishing computational complexity, thus improving the training and classification speed, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Article** & **Preprocessing** & **Classifier** & **Accuracy** & **Sensitivity** & **Specificity** & **PPV** & **F1-Score** \\ \hline [PERSON] et al. [30] & SWT & DCNN & 98.63 & 98.79 & 97.87 & & \\ [PERSON] et al. [31] & RR interval & BiLSTM & 98.51 & 98.32 & 98.67 & & \\ [PERSON] et al. [32] & DWT & SVM & 98.39 & 96.86 & 98.92 & 96.85 & \\ [PERSON] et al. [33] & Pattern adaptive & Hybrid classifiers & 97.8 & 89.41 & & 95.23 & \\ [PERSON] et al. [34] & DWT + ICA & MLP neural & 98.6 & & & \\ [PERSON] et al. [35] & Adversarial autoencoder & Autoencoder & 96.73 & 94.86 & 98.54 & 96.66 \\ Proposed method & DWT (db8), SMOTE & Hybrid ResNet & 99.09 & 97.56 & 99.53 & 95.18 & 96.33 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the proposed method with state-of-the-art ECG classification approaches (all the state-of-the-art ECG classification approaches in the table were conducted on the MIT-BIH Arrhythmia Database).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Class** & **Acc (\%)** & **Sen (\%)** & **Space (\%)** & **PPV (\%)** & **F1-Score (\%)** \\ \hline F & 99.94 & 96.88 & 99.95 & 92.54 & 92.54 \\ N & 99.17 & 99.34 & 98.33 & 99.66 & 99.66 \\ S & 99.53 & 94.81 & 99.65 & 97.37 & 97.37 \\ V & 99.63 & 97.77 & 99.77 & 96.69 & 96.69 \\ Q & 99.9 & 99.01 & 99.97 & 99.63 & 99.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation metrics calculated from the results obtained by executing the model on the test data.