An Optimized Deep Learning Framework for Robust Epileptic Seizure Detection Using EEG Signals
TL;DR
A proposed method for detecting epileptic seizures from electroencephalogram data involves creating an optimal deep learning architecture that incorporates deep learning architectures, feature optimisation, and wavelet-based preprocessing.
Abstract
Epilepsy affects a person's behavior and is characterized by seizures. Epileptic seizures are infamously difficult to treat because of how unpredictable they are. Automatic electroencephalogram (EEG) seizure detection allows medical professionals to better predict when patients may have seizures and adjust treatment plans appropriately. The signals only show one side of the brain, making it impossible to accurately identify epileptic seizures. When done manually, feature extraction also consumes a significant amount of time. To build seizure detection systems reliant on multi-view feature learning via automatic means is not feasible in their absence. A proposed method for detecting epileptic seizures from electroencephalogram data involves creating an optimal deep learning architecture. Using sophisticated hyperparameter tuning techniques, the system incorporates deep learning architectures, feature optimisation, and wavelet-based preprocessing. Results show that compared to previous approaches, ours are much more accurate, robust, and computationally efficient, according to extensive trials conducted on several benchmark datasets. Clinical and real-time deployment of the suggested method is confirmed to be effective by statistical validation.