# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
## 1 Introduction  
Cardiovascular disease remains one of the leading causes of morbidity and mortality among middle-aged and elderly individuals worldwide. Its detrimental effects on the heart, arteries, and venous systems result in alarmingly high rates of mortality and disability. According to the World Health Organization (WHO), cardiovascular disease accounts for approximately 17.9 million deaths annually, representing 32% of global mortality [1]. This sobering statistic underscores the critical need for early prevention and diagnosis. Arrhythmia represents a prevalent and intricate pathological condition within the spectrumof cardiovascular diseases. Accurate classification of electrocardiogram (ECG) signals enables early intervention, thereby substantially reducing mortality risk. Consequently, achieving a higher accurate and cost-effective diagnosis of arrhythmias, along with the assessment of normal heart rhythms, constitutes a primary objective of ongoing research.  
The ECG is a widely utilized, non-invasive diagnostic tool that provides critical physiological insights by recording the heart's electrical activity. Compared to the traditional stethoscope [2], the ECG offers superior resolution and diagnostic accuracy. However, as the volume of ECG data grows and signal complexity increases, manual interpretation has become insufficient to meet clinical demands. Consequently, automated ECG signal classification has emerged as a vital research focus in advancing arrhythmia diagnosis.  
The pathological mechanisms of arrhythmias are multifaceted and diverse, primarily involving abnormal activities in the heart's origin and conduction processes [3]. Based on heart rate differences, arrhythmias are categorized as rapid or slow. Rapid arrhythmias feature a resting heart rate exceeding 100 beats per minute, whereas slow arrhythmias involve a resting heart rate below 60 beats per minute. Per the classification standards of the American Association for the Advancement of Medical Instrumentation (AAMI), non-life-threatening arrhythmias are classified into five types: non-ectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fusion rhythm (F), and unknown type (Q) [4]. These ECG classifications hold significant clinical importance, and their accurate identification is crucial for formulating effective treatment plans.  
In recent years, deep learning (DL) techniques have emerged as promising solutions for the automated classification of ECG signals. Unlike traditional machine learning (ML) approaches, DL algorithms enable direct feature extraction within an end-to-end framework, eliminating the labor-intensive task of manual feature engineering. Notably, the Kolmogorov-Arnold Network (KAN) [5] demonstrates significant promise in addressing complex signal classification tasks, owing to its theoretical interpretability and robust nonlinear mapping capabilities. Nonetheless, research on the application of the KAN in ECG signal classification remains blank, necessitating further investigation into its classification performance and transferability across various arrhythmia categories.  
This study employs the KAN to automate the classification of ECG signals and evaluates its efficacy in arrhythmia classification tasks using the MIT-BIH dataset. To further assess the generalization of KAN, the model was transferred to the PTB dataset for classification predictions in MI diagnosis. Additionally, the performance of the KAN was compared with that of classical machine learning, such as the multi-layer perceptron (MLP) and recent DL, to comprehensively evaluate its classification accuracy, generalization capability, and computational efficiency.  
The remainder of this paper is structured as follows: Section 2 reviews related research on arrhythmia classification; Section 3 details the study's methodology; Section 4 presents the experimental results and comparative analysis; and Section 5 concludes with a summary of the research contributions and prospective future directions.