# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 2 Literature Study  
Artificial intelligence (AI), described by [PERSON] as \"the science and engineering of making intelligent machines\" [6], has profoundly influenced numerous fields over the past two decades, including visual perception, intelligent manufacturing, and knowledge representation and reasoning. In recent years, AI has been increasingly adopted in the medical field due to its robust data analysis capabilities and ability to deeply mine complex patient information, enabling accurate disease prediction and diagnosis through data-driven insights. As a vital branch of AI, machine learning (ML) constructs data models to perform classification tasks by leveraging adaptive learning and evolving mathematical algorithms. However, traditional ML is constrained by its reliance on manual feature extraction, which introduces inherent limitations. Deep learning (DL), an advancement of ML, surpasses traditional methods in data modeling efficiency and accuracy through its multi-layer neural network architecture and capability to automatically learn features from hidden layers. Consequently, DL has become a pivotal tool for medical image classification, disease diagnosis, and medical signal analysis. Nonetheless, the \"black box\" nature of DL [7], referring to the lack of interpretability in its feature learning processes, constrains its potential applications in high-reliability fields like arrhythmia classification. Machine learning has been extensively employed in arrhythmia classification, primarily due to its inherent interpretability. Some representative studies are outlined below. [PERSON] et al. [8] utilized support vector machines (SVMs) for classifying ECG signals in long-term ST datasets. They integrated heart rate variability (HRV) signal extraction and principal component analysis to achieve a classification accuracy of 99.20%. [PERSON] et al. [9] developed the NRSC based on the MIT-BIH dataset, utilizing a discrete wavelet transform for noise reduction and feature extraction, followed by classification using Euclidean Distance, achieving a diagnostic accuracy of 99.32%. For the PTB, [PERSON] et al. [10] extracted nonlinear features using the wavelet transform combined with statistical analysis techniques to optimize classification results, achieving a classification accuracy of 98.80% for distinguishing cardiac diseases from normal signals. The integration of encoders has expanded the diversity of ECG signal classification tasks. Using the PTB, [PERSON] et al. [11] employed stacked sparse autoencoders and Tree Bagger classifiers to enhance classification interpretability. While these machine learning-based approaches have achieved notable success in ECG signal analysis, they often depend on manual feature extraction, which increases model development complexity and limits scalability in large-scale applications. DL has exhibited exceptional performance in ECG signal classification, leveraging automated feature learning and end-to-end training to address these limitations. [PERSON] et al. [12] attained 95.4% accuracy and 98.2% sensitivity on the PTB by integrating a 16-layer convolutional neural network (CNN) with a long short-term memory (LSTM) network. Leveraging the feature-capturing capabilities of neural networks and the attention mechanism enhances the focus and representation of key features. Consequently, [PERSON] et al. [13] introduced an attention-based convolutional neural network (ABCNN), achieving an accuracy of 98.53% on the MIT-BIH dataset. Additionally, [PERSON] et al. [14] developed a transformer-based deep model, ECG DETR, which attained an accuracy of 99.23% on the MIT-BIH. While DL outperforms traditional machine learning in ECG signal analysis, its inherent lack of interpretability remains a significant challenge, reducing clinicians' trust in the model's decision-making process. To address this limitation, this paper investigates the application of the interpretable KAN for classifying ECG signals related to arrhythmia diseases.