Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism

2025/01/17 by Mohammed Guhdar, Guhdar, Mohammed, Ramadhan J. Mstafa +3 · 2 citations
Neuroscience · Psychology · #Artificial intelligence #Brain Tumor Detection and Classification #Computer science #EEG and Brain-Computer Interfaces #Epilepsy #Epileptic seizure #FOS: Computer and information sciences #Geology #Head (geology) #Machine Learning (cs.LG) #Mechanism (biology) #Neuroscience #Pattern recognition (psychology) #Physics #Psychology

paper · pdf · doi:10.48550/arxiv.2501.10342

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/01/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people \citeWHO_epilepsy₅0million. 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 \citeepilepsyfoundation_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.

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