Automatic Epileptic Seizure Detection with MUSIC and Cross-Correlation Methods: Performance Enhancement with Ensemble Learning-Voting

Year 2026, Volume: 15 Issue: 1 , 423 - 437 , 24.03.2026

Güneş Ekim , Nuri İkizler

https://doi.org/10.17798/bitlisfen.1831933

https://izlik.org/JA26HP25LK

Abstract

Epileptic seizures are characterized by abnormal neuronal discharges that generate distinctive patterns in EEG signals, requiring accurate and fast detection for clinical decision support. This study proposes a high-resolution spectral approach that integrates the Multiple Signal Classification (MUSIC) algorithm with cross-correlation-based feature extraction for automated seizure detection. High-resolution spectral estimates of reference EEG signals and individual segments were obtained using the MUSIC algorithm, and six correlation-driven statistical features were computed to capture both spectral similarity and phase relationships. These features were classified using Random Forest, k-Nearest Neighbor, Multilayer Perceptron, and an Ensemble Learning-Voting model. Experiments were conducted on the Bonn University EEG dataset across 14 binary and multi-class tasks. The Ensemble Learning-Voting classifier achieved the best overall performance with an average accuracy of 99.17%, outperforming individual classifiers. The proposed methodology provides high frequency resolution, low computational cost, and robust classification capability, demonstrating strong potential for real-time epileptic seizure detection and integration into clinical EEG monitoring systems.

Keywords

Epileptic seizure detection , Cross-correlation technique , MUSIC method , Machine learning , Ensemble learning voting

Ethical Statement

The study is complied with research and publication ethics.

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