# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 4 Experimental Results and Analysis
### Experimental Results  
The ECG signal classification experiments using the KAN were performed on the original MIT-BIH dataset, the original PTB dataset, and their respective versions after balanced and variance sorting. To demonstrate its superiority, we conducted comparative experiments with an MLP, and the results are presented in Tables 2-5. As shown in Table 2, on the original MIT-BIH dataset, the average accuracy (97.68%) and precision (93.19%) of the KAN exceed those of MLP (97.62% and 87.06%), with a particularly notable improvement in precision. This demonstrates that the KAN achieves higher accuracy and precision in classifying specific categories of unbalanced raw data and effectively captures arrhythmia features. Moreover, the F1 score (87.50%) is slightly better than that of MLP (85.08%), further highlighting the overall superior performance of the KAN. Although the recall rate of the KAN (82.47%) is slightly lower than that of MLP (83.18%), the KAN's performance surpasses MLP after data balancing and variance sorting. As shown in Table 3, the KAN achieves a precision of 99.07% and recall of 98.99%, compared to MLP's precision of 98.22% and recall of 98.14%. The processed KAN achieves an F1 score of 99.03% and an average accuracy of 99.08%, demonstrating superior adaptability and feature extraction capabilities for processed signal data. These findings suggest that \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** \\ \hline MLP & 96.01 & 95.06 & 95.26 & 95.16 \\ KAN & 97.68 & 97.90 & 97.97 & 97.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of classification results on the original PTB. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** \\ \hline MLP & 97.62 & 87.06 & 83.18 & 85.08 \\ KAN & 97.68 & 93.19 & 82.47 & 87.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification results on the original MIT-BIH. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1\_Score (\%)** \\ \hline MLP & 98.28 & 98.22 & 98.14 & 98.21 \\ KAN & 99.08 & 99.07 & 98.99 & 99.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification results on the original PTB. the KAN's performance is fully optimized after data distribution adjustments, allowing it to excel in a more balanced environment. In the classification of arrhythmia diseases using ECG signals, the KAN's nonlinear feature extraction capabilities can more accurately capture subtle pattern variations in ECG signals. Notably, the KAN demonstrates unique advantages in distinguishing categories with similar signal morphologies yet significant pathological differences. To demonstrate the transferability and generalization capability of the KAN in ECG signal classification tasks, the KAN exhibited clear advantages on the original PTB dataset. As shown in Table 4, the KAN achieves a precision of 97.90% and recall of 97.97%, surpassing MLP's precision of 95.06% and recall of 95.26%. The F1 score and average accuracy improved to 97.97% and 97.68%, respectively. Tables 2 and 4 demonstrate that the KAN is more effective in extracting key features from complex ECG signals. Notably, the KAN's classification performance is further optimized after data balanced and variance sorting. Similarly, as shown in Table 5, the KAN achieves an accuracy of 99.11%, with precision and recall exceeding 98.82%, slightly better than MLP's accuracy (97.40%), precision (97.45%), and recall (97.35%). These results indicate that the KAN is better suited to adapt to the characteristic changes in ECG signals after data processing. To further demonstrate the model's stability and robustness during training, we analyzed the average accuracy fit between the training and validation sets for both models across different datasets, as shown in Appendix A.1.