Hybrid Kolmogorov-Arnold and convolutional neural network model for single-lead electrocardiogram classification

Abstract

This study proposes a hybrid Kolmogorov-Arnold networks (KANs) and convolutional neural networks (CNN) to classify electrocardiogram (ECG) signal abnormalities in one lead ECG data of wearable telemedicine. The hybrid model combines CNN to extract hierarchical features from sequential data and KANs to model non-linear relationships with fewer parameters as an efficient classification. The study explores the model’s capacity to balance accuracy, computational efficiency, and memory usage as critical factors for real-time health monitoring in resource-constrained environments on the single-lead MIT-Beth Israel hospital (MIT-BIH) Supraventricular Arrhythmia database with five different class labels. For comparison, standalone CNN and KAN models were also trained on the same balanced dataset. The CNN model achieved an accuracy of 96.62%, precision of 96.81%, and recall of 96.53%. The KAN model, while computationally efficient, performed less effectively, with an accuracy of 94.15%, precision of 95.01%, and recall of 92.57%. In contrast, our hybrid KAN-CNN model outperformed both, attaining an accuracy of 97.53%, precision of 97.66%, recall of 97.40%, and a low loss of 0.0840. The study also explores the impact of quantization and compression on model performance, revealing that both CNN and Hybrid KAN-CNN models retained high accuracy post-quantization, whereas the KAN model exhibited a more significant drop in performance.