# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 5 Discussion  
This study proposed an interpretable KAN for the classification of ECG signals and evaluated its performance and adaptability using the MIT-BIH and PTB datasets. The findings highlight that data preprocessing plays a critical role in enhancing classification outcomes. By applying balanced and variance sorting, the data distribution was effectively optimized, significantly improving the KAN's feature representation capabilities. On the MIT-BIH, the classification accuracy reached 99.08%, with a precision rate of 99.07%, while on the PTB, both classification accuracy and precision reached 99.11%. These results underscore the importance of data preprocessing and demonstrate the robustness and transferability of the KAN across different datasets.  
To further analyze the performance of the KAN, this study compared the classification results of the KAN and MLP models under four experimental conditions. The results reveal that the KAN exhibited superior fitting capabilities during both the training and testing phases, achieving significantly higher accuracy than MLP. Additionally, the training process of the KAN was more stable, effectively avoiding overfitting. Furthermore, to validate the discriminative power of the features extracted by the KAN, hierarchical clustering, density clustering, and spectral clustering were applied to analyze its feature vectors. A comparison between the clustering distributions of original features and KAN-extracted features reveals significant improvements in capturing key mode points and the underlying distribution patterns of ECG signals, validating the KAN's adaptability for classification tasks in complex signal processing. The experiments demonstrated that, compared to the original data, the feature boundaries extracted by the KAN were more distinct and better support ECG signal-based disease classification tasks.  
Compared to existing research results (e.g., [PERSON] et al. [26] and [PERSON] et al. [27]), the KAN demonstrates significant advantages across multiple metrics. On the MIT-BIH, its classification accuracy is notably higher, and its precision reaches a state-of-the-art level. Similar advancements are observed in the PTB, further validating its robustness. These improvements are evident not only in classification performance but also in the model's convergence behavior. Experimental results indicate that, regardless of whether the data undergoes preprocessing, the KAN exhibits a smooth and rapid convergence curve during training, highlighting its stability and adaptability in complex data scenarios.  
The findings of this study propose a novel approach for disease classification and triage based on ECG signals and reveal the potential of the KAN for wide application in diagnosing conditions such as arrhythmias and heart failure. Future research will focus on integrating the KAN with other network architectures, such as convolutional neural networks, to enhance classification performance. Additionally, large-scale and diverse datasets will evaluate the model's robustness. Efforts will also aim to strengthen the interpretability of the model, providing deeper insights to support clinical decision-making. Developing real-time monitoring modules represents another critical direction, enabling early warning and rapid diagnosis of arrhythmias.