چکیده مقاله
Epilepsy is a chronic neurological disorder characterized by recurrent, unpredictable seizures and is commonly diagnosed through electroencephalogram EEG signal analysis Accurate, automated detection of epileptic patterns in EEG signals is essential for timely diagnosis and effective treatment In this study, we propose a robust framework for epilepsy detection leveraging Discrete Wavelet Transform DWT for feature extraction and Genetic Algorithm GA for optimal feature selection EEG signals are first preprocessed and decomposed into multiple sub bands using DWT to extract temporal, spectral, and time frequency domain features These features are then optimized via GA, which reduces dimensionality by selecting the most discriminative attributes The selected features are then fed to Support Vector Machine SVM , K Nearest Neighbors KNN , and Artificial Neural Network ANN classifiers The proposed method was evaluated using the Bonn EEG dataset and validated through 10 fold cross validation Results demonstrate that the SVM classifier achieved the highest accuracy of 97 8% and an AUC of 0 989 Our approach significantly outperforms traditional methods, showing enhanced efficiency and reliability due to effective feature selection This study underscores the potential of integrating wavelet based feature extraction with evolutionary algorithms to develop intelligent EEG based diagnostic systems for epilepsy detection
کلیدواژهها
نویسندگان
Fatemeh Yekta Asaei
Department of Biomedical Engineering, Ma C , Islamic Azad University, Mashhad, Iran
Mahdi Azarnoosh
Department of Biomedical Engineering, Ma C , Islamic Azad University, Mashhad, Iran
شیوه ارجاع
Yekta Asaei, Fatemeh and Azarnoosh, Mahdi,1404,Epileptic Seizure Classification Using Wavelet -Based Features and Evolutionary Feature Selection with Machine Learning Classifiers,21st International Conference on Innovation and Research in Engineering Sciences
ارائهشده در
مجموعه مقالات بیست و یکمین کنفرانس بین المللی نوآوری و تحقیق در علوم مهندسی31 تیر 1404