Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
This work addresses the challenge of improving seizure classification to aid in diagnosis and management for individuals with epilepsy, potentially enhancing safety and employment opportunities, though it appears incremental in method.
The paper tackles the problem of classifying epileptic seizures from EEG signals by proposing a hybrid deep learning model combining 1D-CNN with a multi-head attention mechanism, but no concrete results or numbers are provided in the 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.