From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks

A comprehensive review examines how Kolmogorov-Arnold Networks (KANs) can overcome critical limitations in deep learning-based EEG seizure detection, offering improved interpretability, parameter efficiency, and performance under data scarcity constraints. The research positions KANs as a paradigm shift necessary for deploying transparent, clinically viable seizure detection systems in wearable and implantable neuromodulation devices.

The article addresses a significant gap between laboratory advances in deep learning and real-world clinical deployment of EEG seizure detection systems. Traditional deep learning models, while achieving high accuracy in controlled settings, suffer from three fundamental limitations: they operate as black-boxes incompatible with clinical decision-making requirements, they require massive annotated datasets often unavailable in medical practice, and they demand computational resources unsuitable for resource-constrained wearable devices that patients need for continuous monitoring. This represents a critical bottleneck in translating AI research into practical medical applications where interpretability and physician trust are paramount.

Kolmogorov-Arnold Networks introduce a novel architecture that replaces fixed activation functions with learnable, flexible functions along network connections. This architectural change directly addresses the identified limitations by dramatically reducing parameter requirements while maintaining or improving predictive accuracy. The inherent mathematical transparency of KANs enables physicians to understand decision pathways, fulfilling regulatory and clinical requirements for explainable AI in healthcare.

The implications extend across medical device development, wearable technology, and implantable neuromodulation systems. Manufacturers can develop more efficient devices with extended battery life and reduced processing demands. Healthcare providers gain tools that combine high accuracy with clinical interpretability, potentially accelerating adoption of AI-assisted seizure monitoring in routine practice. The research trajectory toward patient-specific, transparent monitoring systems could reshape epilepsy care delivery and establish precedents for interpretable AI across medical domains.

• →KANs replace fixed activation functions with learnable functions, enabling both accuracy and mathematical interpretability in EEG seizure detection.
• →Traditional deep learning models require extensive annotated data and computational resources incompatible with wearable and implantable medical devices.
• →KAN architectures achieve exceptional parameter efficiency, enabling deployment on resource-constrained clinical devices with extended operational capability.
• →Interpretability provided by KANs addresses the critical barrier of physician trust and clinical adoption in medical AI applications.
• →This paradigm shift enables next-generation patient-specific epilepsy monitoring systems that balance predictive performance with transparency requirements.