Affiliation:
1. School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
2. College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Abstract
Neurological diseases are a significant health threat, often presenting through abnormalities in electroencephalogram (EEG) signals during seizures. In recent years, machine learning (ML) technologies have been explored as a means of automated EEG pathology diagnosis. However, existing ML-based EEG binary classification methods largely focus on extracting EEG-related features, which may lead to poor performance in classifying EEG signals by overlooking potentially redundant information. In this paper, we propose a novel Kruskal–Wallis (KW) test-based framework for EEG pathology detection. Our framework first divides EEG data into frequency sub-bands using wavelet packet decomposition and then extracts statistical characteristics from each selected coefficient. Next, the piecewise aggregation approximation technique is used to obtain the aggregated feature vectors, followed by the KW statistical test methodology to select significant features. Finally, three ensemble learning classifiers, random forest, categorical boosting (CatBoost), and light gradient boosting machine, are used to classify the extracted significant features into normal or abnormal classes. Our proposed framework achieves an accuracy of 89.13%, F1-score of 87.60%, and G-mean of 88.60%, respectively, outperforming other competing techniques on the same dataset, which shows the great promise in EEG pathology detection.
Funder
National Natural Science Foundation of China
National Key Research and Development Program of China
Subject
General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)
Reference51 articles.
1. Schomer, D.L., and Da Silva, F.L. (2012). Niedermeyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams and Wilkins.
2. Epileptic seizure detection using hybrid machine learning methods;Subasi;Neural Comput. Appl.,2019
3. Use of principal component analysis for automatic classification of epileptic EEG activities in wavelet framework;Acharya;Expert Syst. Appl.,2012
4. A decision support system for automated diagnosis of Parkinson’s disease from EEG using FAWT and entropy features;Chawla;Biomed. Signal Process. Control,2023
5. Automated EEG-based screening of depression using deep convolutional neural network;Acharya;Comput. Methods Programs Biomed.,2018
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