International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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A Comparative Analysis of Rank based Feature Selection Methods for Epileptic Seizure Prediction using EEG Signals

Author(s) Prof. Neeta Hemant Chapatwala, Prof. Dr. Chirag N. Paunwala
Country India
Abstract Epilepsy prediction addresses the unpredictable nature of seizures, which disrupt brain function and pose risks like loss of consciousness or injury. Accurate forecasting enables timely interventions, such as medication or alerts, to prevent harm and enhance patient safety. Raw EEG-based epilepsy prediction generates many redundant and irrelevant features, which can degrade model performance. Feature selection methods enhance epilepsy prediction from EEG signals by identifying the most discriminative features, reducing dimensionality, and improving classifier sensitivity and accuracy. In order to overcome difficulties in managing high-dimensional, noisy EEG signals for epilepsy prediction, proposed work synthesizes time-domain, frequency-domain, and nonlinear feature extraction employing discrete wavelet transform (DWT) for band extraction and concentrating on ANOVA test, mRMR (Minimum-Redundancy–Maximum-Relevance), and Chi-square approaches for epilepsy prediction. Results show sensitivity of 97.34 % and accuracy of 93.60% with mRMR method which is higher than ANOVA and chi square. These methods effectively reduce feature dimensionality, enhancing computational efficiency and model interpretability, while multi-domain feature fusion further improves detection performance. However, variability in EEG data and limited generalizability across heterogeneous datasets remain challenges.
Keywords Epilepsy, Feature selection, mRMR, ANOVA, Chi-square.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-12-22
DOI https://doi.org/10.36948/ijfmr.2025.v07i06.64321
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E-ISSN 2582-2160
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