# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 4 Experimental Results and Analysis
### Experimental Results
The accuracy fitting analysis in Appendix A.1 reveals that in four cases, the accuracy fit between the training and validation sets for the KAN is significantly better than that of MLP. On the MIT-BIH dataset, the accuracy fit between the training and validation sets was nearly identical, with the smoothest data and minimal fluctuations in accuracy. However, on both the MIT-BIH and PTB datasets, MLP exhibited the lowest accuracy fit during the early stages of the fitting process (epochs <30), accompanied by significant fluctuations in validation set accuracy. For epochs >30, MLP's accuracy interpolation was significantly higher than that of the KAN.  
Consequently, in classifying arrhythmia diseases using ECG signals, the KAN demonstrates higher accuracy and superior robustness and fitting stability than MLP. This analysis underscores the KAN's superiority in handling ECG signal classification tasks, as evidenced by its overall performance and accuracy fitting. To evaluate the classification performance for specific categories of heart failure, we plotted the confusion matrix [24] and ROC curve [25] for the MIT-BIH dataset after balanced and variance sorting, highlighting the KAN's superiority in classification precision across various heart failure categories, as shown in Figure 5.  
On the MIT-BIH dataset, after balanced and variance sorting, the overall classification performance of the KAN surpasses that of MLP. As depicted in Figure 5, MLP demonstrates high sensitivity to categories 3 and 1, achieving accuracies exceeding 99%. However, its classification performance for categories 0, 1, and 2 is relatively weaker, particularly for category 0, where accuracy only reaches 94.63%. Furthermore, Figure 5a reveals that category 0 is frequently misclassified as category 1. Although the KAN also exhibits occasional misclassification for category 0, its recognition accuracy improves significantly, reaching 96.77%. Moreover, the KAN achieves classification accuracies exceeding 99% for categories 1, 3, and 4, while maintaining a strong performance of 98.99% for category 2, as shown in Figure 5c. In Figure 5b, the AUC values for MLP across all categories range between 0.9962 and 0.9998, indicating its solid classification performance on the dataset post-processing. However, category 0 shows the lowest AUC value, suggesting a slightly weaker discriminatory capability in this category. In contrast, Figure 5d demonstrates that the AUC values for all categories with the KAN are exceptional, reaching 0.999, which approaches the theoretical upper-performance limit for classification models. The ROC curve for the KAN is near the upper-left corner, reflecting balanced and superior classification performance across all categories. Figure 5 illustrates that the KAN outperforms MLP in terms of ROC curve performance, particularly when the discrimination performance nears the upper limit. This highlights the KAN's enhanced adaptability for feature extraction and its ability to effectively separate categories, further cementing its advantage in ECG signal classification tasks. In summary, balanced and variance sorting of the dataset significantly enhance the model's ability to distinguish between different categories, particularly in addressing the challenges posed by uneven category distribution. While MLP demonstrates a commendable capacity to classify ECG signals, it still exhibits misclassification issues for certain categories. In contrast, the KAN outperforms MLP across all categories, achieving a remarkably low misclassification rate and presenting an ROC curve that approaches the ideal state. It indicates the KAN's superior classification capability for heart failure signals. Given its exceptional classification performance and strong generalization ability, the KAN is well-suited for application in high-risk clinical scenarios. Its reliable and accurate classification makes it a valuable tool for supporting the automatic diagnosis of heart failure, offering robust and trustworthy assistance in medical decision-making.