Hybrid Deep Learning Model for epileptic seizure classification by using 1D-CNN with multi-head attention mechanism
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Abstract (arXiv)
Epilepsy is a prevalent neurological disorder globally, impacting around 50 million people citeWHOepilepsy50million. 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 citeepilepsyfoundationmyoclonic. 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 46 references that are in the map. First posted on arXiv 17 Jan 2025.
Builds on
6 of 46 references.
References · 6
- 2020Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlationcited by 420
- 2019Transfusion: Understanding Transfer Learning for Medical Imagingcited by 366
- 2018ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identificationcited by 27
- 2022The choice of scaling technique matters for classification performancecited by 25
- 2024Real-time EEG-based Emotion Recognition Model using Principal Component Analysis and Tree-based Models for Neurohumanitiescited by 2
- 2023Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processingcited by 1
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Citing papers per year: 2026: 1.
1 paper.