# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 4 Experimental Results and Analysis
### Comparison with the Existing Models of the Literature  
We conducted a comparative analysis of the results from various prior studies on the MIT-BIH and PTB datasets. Evidently, the KAN, after balanced and variance sorting, demonstrates superior performance in classifying ECG signals. Table 6 presents the state-of-the-art results achieved on the MIT-BIH, while Table 7 highlights the advanced outcomes on the PTB. Figure 6: Visualization effects of three clustering methods on ECG signal data and feature vectors. The accuracy of the KAN in Table 6 reaches 99.08%, which is 0.43% higher than the 98.66% achieved by the highest-performing study from [PERSON] et al. [30]. It also shows improvements of 0.44% and 0.45% over the results of [PERSON]. et al. [13] and [PERSON] et al. [27], respectively. The most notable gains are observed when compared to the accuracies of [PERSON] et al. [16] (93.40%) and [PERSON] et al. [32] (86.67%), with increases of 6.08% and 14.32%, respectively. In terms of classification precision, the KAN achieves the highest value of 99.07%, surpassing the second-best result of 98.92% by [PERSON] et al. [30] by 0.15%. The improvements are particularly significant compared to [PERSON] et al. [32], showing an increase of 14.74%. However, the recall rate of the KAN is not the highest, with [PERSON] et al. [26] and [PERSON] et al. [29] achieving 99.77% and 99.70%, respectively. Nonetheless, the KAN demonstrates comparable recall performance to [PERSON] et al. [27], and its recall rate shows a significant improvement over several other models. Overall, the KAN achieves substantial improvements in accuracy and precision, marking a new performance benchmark for ECG signal classification. While its recall rate is slightly lower than the top-performing models, the gap is minimal. For classification tasks in medical signal processing (especially ECG signal classification) enhancements in accuracy are crucial, as even small improvements can greatly reduce misdiagnosis and missed diagnosis rates. Compared to studies with lower performance (e.g., [PERSON] et al. [16] and [PERSON] et al. [32]), the KAN exhibits superior adaptability, highlighting its suitability for complex categories and imbalanced data distributions. To further validate the universality and generalization capability of the proposed ECG signal classification strategy, we conducted comparative analyses on the PTB, as shown in Table 7.  
The statistical results in Table 7 further confirm that the KAN is highly suitable for the classification of ECG signals, demonstrating its advanced performance. Notably, the improvements in accuracy and precision are particularly significant. While [PERSON] et al. [33] achieved a precision of 99.00%, it remains slightly lower than the KAN's 99.11%. In terms of recall, the KAN outperforms existing classification methods, with a best result of 98.82%, marginally exceeding [PERSON] et al. [34]'s 98.00%. Taken together, the \begin{table}
\begin{tabular}{c c c c} \hline
**Works** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline [PERSON] et al. [26] & 98.30 & 90.00 & 99.77 \\ [PERSON] et al. [27] & 98.64 & \(\backslash\) & 99.00 \\ [PERSON] et al. [28] & 98.29 & \(\backslash\) & \(\backslash\) \\ [PERSON] et al. [29] & 98.00 & 95.80 & 99.70 \\ [PERSON] et al. [30] & 98.66 & 98.92 & 93.88 \\ [PERSON] et al. [16] & 93.40 & \(\backslash\) & \(\backslash\) \\ [PERSON] et al. [31] & 98.19 & 97.58 & 97.66 \\ [PERSON] et al. [32] & 86.67 & 86.34 & 86.67 \\ [PERSON] et al. [13] & 98.65 & 98.68 & 98.65 \\ our & 99.08 & 99.07 & 98.99 \\ \hline \end{tabular}
\end{table} 
Table 6: ECG signal classification results on the MIT-BIH.