Ensemble DL Techniques for EEG Epilepsy Classification: Utilizing HRPCS for Enhanced Feature Selection and Improved Accuracy in Seizure Detection and Diagnosis

Abstract

Recurrent seizures are an indicator of epilepsy, a complicated neurological illness that requires prompt and precise diagnosis for successful treatment. Electroencephalogram (EEG) signals are critical in identifying seizure types and patterns, yet the analysis of these signals poses significant challenges. The variability in brain activity, the presence of noise and artifacts, and the complexity of differentiating between epileptic and non-epileptic seizures complicate accurate classification. Conventional methods often fall short, leading to misdiagnoses and inadequate treatment plans. To address these challenges, this study proposes a robust classification model for detecting and classifying EEG epilepsy data. The methodology begins with rigorous data preprocessing, which includes data cleaning and normalization to enhance the signal quality and ensure consistency across the dataset. Next, use dynamic and statistical feature extraction techniques to obtain key EEG signal parameters, which are necessary for accurately differentiating between seizure types. Furthermore, implement a Hybrid Red Piranha Cuckoo Search (HRPCS) algorithm for feature selection, allowing us to identify the most relevant features while reducing dimensionality. Finally, hybrid deep learning techniques HCRNN are utilized, incorporating Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) for classification and prediction tasks. Blockchain technology is used to secure EEG data, ensuring integrity and patient privacy. After classification, the model also incorporates predictive analytics to forecast potential future seizures, enhancing patient management strategies.