Eigenvalue Distribution-Based Approach for EEG Signal Analysis: Noise Removal and Epileptic Seizure Discrimination

Abbas Adib; Khaled Abohashem; Nayeb Takroni

Authors

Abbas Adib Microbiology Department, Riyadh Regional Laboratory & Blood Bank, Riyadh, Saudi Arabia
Khaled Abohashem Pathology and Clinical Laboratory Administration, King Saud Medical City, Riyadh, Saudi Arabia
Nayeb Takroni Research & Innovation Center, King Saud Medical City, Riyadh, Saudi Arabia

Abstract

This paper presents a novel approach for the analysis of electroencephalography (EEG) signals based on the distribution of eigenvalues of a scaled Hankel matrix. The proposed approach enables the determination of the number of eigenvalues required for noise removal and signal extraction in singular spectrum analysis. It explores the applicability of the approach in discriminating between epileptic seizure and normal EEG signals, extracting attractive patterns, filtering EEG signals, and eliminating noise. Various criteria are utilized as features to distinguish between epileptic and normal EEG segments. The experimental results demonstrate the capability of the approach for effective noise removal in EEG signals and successful discrimination between epileptic and normal segments. This approach offers a promising solution for enhanced EEG signal analysis and has potential applications in neurological disorder diagnosis and treatment.

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