Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
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.
Code (0)
등록된 구현이 없습니다.
Tasks
EEGSimilar Papers 제목 키워드 기반
Residual and bidirectional LSTM for epileptic seizure detection
Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy d…
EEGElectroencephalogram (EEG)Seizure DetectionEpileptic Seizure Classification with Symmetric and Hybrid Bilinear Models
Epilepsy affects nearly 1% of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly special…
ClassificationDiagnosticGeneral ClassificationA New Method for Epileptic Seizure Classification in EEG Using Adapted Wavelet Packets
Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be be…
ClassificationEEGElectroencephalogram (EEG)Classification of epileptic seizures in EEG data based on iterative gated graph convolution network
Introduction: The automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay amon…
EEGElectroencephalogram (EEG)Epileptic Seizures Detection Using Deep Learning Techniques: A Review
A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of ar…
Cloud ComputingDeep LearningEEGElectroencephalogram (EEG)+1