Akademik Veri Yönetim Sistemi
Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, sa.Advanced Online Publication, ss.1-15, 2026 (TRDizin)
Automated detection of epileptic seizures from electroencephalogram signals plays a critical role in clinical decision support and continuous patient monitoring. In this study, a novel hybrid feature extraction method is proposed, combining Linear Predictive Coding coefficients and statistical descriptors from Discrete Wavelet Transform sub-bands to enhance the representational capacity of EEG signals. Unlike conventional approaches that rely solely on spectral, time-domain, or entropy-based features, this method captures both temporal dynamics and localized frequency characteristics of the signal. The system was developed and evaluated using the publicly available Bonn EEG dataset, which includes both healthy and epileptic recordings in controlled conditions. Each 4096-sample EEG segment was split into two equal parts to increase data volume, resulting in 1000 segments for analysis. Feature vectors were classified using an ensemble model based on majority voting across four classifiers: Random Forest, Support Vector Machine, Multilayer Perceptron and k-Nearest Neighbors. Performance was assessed across 16 binary and multi-class classification tasks using accuracy and Matthews Correlation Coefficient as evaluation metrics. The proposed hybrid approach consistently outperformed individual feature types in all tasks, achieving up to 100% accuracy and perfect Matthews Correlation Coefficient in seizure vs. non-seizure classifications. These findings highlight the effectiveness of integrating Linear Predictive Coding and Discrete Wavelet Transform features in a lightweight and interpretable ensemble architecture, offering a promising solution for accurate and scalable seizure detection in clinical and portable settings.Automated detection of epileptic seizures from electroencephalogram signals plays a critical role in clinical decision support and continuous patient monitoring. In this study, a novel hybrid feature extraction method is proposed, combining Linear Predictive Coding coefficients and statistical descriptors from Discrete Wavelet Transform sub-bands to enhance the representational capacity of EEG signals. Unlike conventional approaches that rely solely on spectral, time-domain, or entropy-based features, this method captures both temporal dynamics and localized frequency characteristics of the signal. The system was developed and evaluated using the publicly available Bonn EEG dataset, which includes both healthy and epileptic recordings in controlled conditions. Each 4096-sample EEG segment was split into two equal parts to increase data volume, resulting in 1000 segments for analysis. Feature vectors were classified using an ensemble model based on majority voting across four classifiers: Random Forest, Support Vector Machine, Multilayer Perceptron and k-Nearest Neighbors. Performance was assessed across 16 binary and multi-class classification tasks using accuracy and Matthews Correlation Coefficient as evaluation metrics. The proposed hybrid approach consistently outperformed individual feature types in all tasks, achieving up to 100% accuracy and perfect Matthews Correlation Coefficient in seizure vs. non-seizure classifications. These findings highlight the effectiveness of integrating Linear Predictive Coding and Discrete Wavelet Transform features in a lightweight and interpretable ensemble architecture, offering a promising solution for accurate and scalable seizure detection in clinical and portable settings.