KAN–EEG: towards replacing backbone–MLP for an effective seizure detection system

The landscape of artificial intelligence (AI) research is witnessing a transformative shift with the emergence of the Kolmogorov-Arnold network (KAN), presenting a novel architectural paradigm aimed to redefine the structural foundations of AI models, which are based on multilayer perceptron (MLP). Through rigorous experimentation and evaluation, we introduce the KAN-electroencephalogram (EEG) model, a tailored design for efficient seizure detection. Our proposed network is tested and successfully generalized on three different datasets, one from the USA, one from Europe, and one from Oceania, recorded with different front-end hardware. All datasets are scalp EEG in adults and are from patients living with epilepsy. Our empirical findings reveal that while both architectures demonstrate commendable performance in seizure detection, the KAN model exhibits high-level out-of-sample generalization across datasets from diverse geographical regions, underscoring its inherent efficacy and adaptability at the backbone level. Furthermore, we demonstrate the resilience of the KAN architecture to model size reduction and shallow network configurations, highlighting its versatility and efficiency by preventing over-fitting in-sample datasets. This study advances our understanding of innovative neural network architectures and underscores the pioneering potential of KANs in critical domains such as medical diagnostics.