Detection of Epileptic Spikes using the Wavelet-RLS Hybrid Method

Abstract

Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal neural activity in the brain. Accurate detection of epileptic spikes in EEG signals is crucial for effective treatment. In this study, we propose a hybrid method that combines wavelet transform with Recursive Least Squares (RLS) adaptive filtering to enhance EEG signals by removing artifacts. The method's performance was evaluated using the Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE), demonstrating superior artifact removal performance compared to traditional approaches. Wavelet-based statistical features were then extracted and classified using a Probabilistic Neural Network (PNN), which is recognized for its high accuracy and computational efficiency. The proposed method achieved a classification accuracy of approximately 91.86% on real-world EEG data.