Enhanced Feature Selection for EEG-based Seizures Classification Through Ensemble Learning and Mutual Information

A Feature Selection and Hyperparameter Optimization Framework for Multiclass EEG-based Epilepsy Detection

Epilepsy is a neurological disorder characterized by recurrent seizures, and EEG continues to be the gold standard for diagnosis in clinical practice. However, the nonlinear nature of the EEG and its sensitivity to noise make manual interpretations difficult. This study presents a feature selection and hyperparameter o...

An Optimized Deep Learning Framework for Robust Epileptic Seizure Detection Using EEG Signals

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.

Electroencephalography-based epileptic seizure classification using a sparse iterative recursive-gated recurrent unit

Epileptic seizure classification using electroencephalography (EEG) plays a significant role in clinical diagnosis, long-term patient monitoring, and treatment planning. However, accurate seizure classification remains challenging due to its high complex and non-stationary nature of brain signal, which leads to misdiag...

Comprehensive Feature Fusion in Deep Learning Models for Robust Epileptic Seizure Detection from EEG Signals

Epilepsy is a prevalent neurological disorder characterized by recurrent epileptic seizures. After a stroke, it is a highly common neurological condition. For identifying seizures, the Electroencephalogram (EEG) is the gold-standard modality for capturing and recording the brain's electrical activity with high temporal...

AI-Driven Real-Time EEG Analysis for Epileptic Seizure Prediction using Optimal Features

Over 50 million people worldwide suffer from epilepsy, making it one of the most prevalent neurological disorders. Accurate identification and prediction of epileptic seizures are crucial for reducing health-related risks, improving patient safety, and enabling timely clinical intervention. Electroencephalography (EEG)...

Seizure Classification from ECG Signals using CNN-Bi-LSTM with SHAP-based Explainability