Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection

Yunning Zhong, Hongyu Wei, Lifei Chen () and Tao Wu ()
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Yunning Zhong: School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China
Hongyu Wei: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Lifei Chen: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Tao Wu: School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, China

Mathematics, 2023, vol. 11, issue 7, 1-17

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.

Keywords: pathology detection; significant features; Kruskal–Wallis test; wavelet packet decomposition; electroencephalography (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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