LeadKAN: A Low-Rank Kernelized Kolmogorov–Arnold Network for Efficient Nonlinear Representation of Electrocardiogram Signals

Abstract

Electrocardiography (ECG) is a widely used, non-invasive tool for assessing cardiac function, but conventional disease-centric models do not fully capture overall cardiovascular health. Recent work has introduced the concept of ECG age: a neural network–predicted age derived from ECG signals. Its difference from chronological age, known as delta age ($\Delta$Age), has emerged as a surrogate marker of cardiovascular well-being. While deep learning approaches have shown promise for ECG age estimation, their computational complexity and lack of interpretability limit deployment in compute-constrained clinical environments. Kolmogorov–Arnold Networks (KANs) offer parameter efficiency and improved interpretability, yet existing variants remain compute-heavy, underexplored for regression tasks, and unable to disentangle contributions from individual ECG leads. To address these challenges, we propose LeadKAN, a lightweight and explainable KAN architecture for ECG age estimation. LeadKAN is built on LoRKAN layers, a novel layer design that replaces fully connected layers with low-rank bilinear mixing followed by an RBF-kernelized top, significantly reducing parameter count and computation. LeadKAN achieves ECG age estimation performance (MSE $\approx$ 112; MAE $\approx$ 8.25 years) comparable to state-of-the-art models, while requiring 16× fewer parameters and 45× fewer multiply–add operations. Additionally, lead-specific encoders enable attribution analysis, thereby enhancing clinical interpretability. These results position LeadKAN as an efficient and explainable framework for ECG age estimation, with strong potential for deployment in real-world, compute-limited settings.