A review arguing that Kolmogorov-Arnold Networks address key limitations of deep learning models for EEG-based seizure detection through improved interpretability and efficiency.
Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \cite{WHO_epilepsy_50million}. Epileptic seizures result from sudden abnormal electrical activity in the brain, which can be read as sudden and significant changes in the EEG signal of the brain. The signal can vary in severity and frequency, which results in loss of consciousness and muscle contractions for a short period of time \cite{epilepsyfoundation_myoclonic}. Individuals with epilepsy often face significant employment challenges due to safety concerns in certain work environments. Many jobs that involve working at heights, operating heavy machinery, or in other potentially hazardous settings may be restricted for people with seizure disorders. This certainly limits job options and economic opportunities for those living with epilepsy.
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
From Handcrafted Features to Functional Edge Learning: Evolution of EEG Seizure Detection Frameworks
A review arguing that Kolmogorov-Arnold Networks address key limitations of deep learning models for EEG-based seizure detection through improved interpretability and efficiency.