Feature Engineering for Epileptic Seizure Classification Using SeqBoostNet

Abstract

Epileptic seizure, a severe neurological condition, profoundly impacts patient's social lives, necessitating precise diagnosis for classification and prediction. This research addresses the critical gap in automated seizure detection for epilepsy patients, aiming to improve diagnostic accuracy and prediction capabilities through Artificial Intelligence driven analysis of Electroencephalography (EEG) signals. The system employs innovative feature combination such as spectral and temporal features, combining Uniform Manifold Approximation and Projection (UMAP) with Fast Fourier Transformation (FFT), and a classification technique called Sequential Boosting Network (SeqBoostNet). SeqBoostNet is a groundbreaking stacked model that integrates machine learning (ML) and deep learning (DL) approaches, leveraging the strengths of both methodologies to swiftly differentiate seizure onsets, events, and healthy brain activity. The method's efficacy is validated on benchmark datasets such as BONN from the UCI repository and real-time data BEED from the Bangalore EEG Epilepsy Dataset, achieving remarkable accuracy rates of 98.40% for BONN and 99.66% for BEED datasets. The practical significance of this study lies in its potential to transform epilepsy care by providing a precise automated seizure detection system, ultimately enhancing diagnostic accuracy and patient outcomes. Furthermore, it underscores the importance of integrating advanced AI techniques with EEG analysis for more effective neurological diagnostics and treatment strategies.