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Multi-dimensional feature extraction of EEG signal and its application in stroke classification

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Bibliographic Details
Title: Multi-dimensional feature extraction of EEG signal and its application in stroke classification
Authors: Teng Wang, Wenhui Jia, Fenglian Li, Xirui Liu, Xueying Zhang, Fengyun Hu
Source: Scientific Reports, Vol 15, Iss 1, Pp 1-17 (2025)
Publisher Information: Nature Portfolio, 2025.
Publication Year: 2025
Collection: LCC:Medicine
LCC:Science
Subject Terms: Stroke classification, EEG, Autocorrelation feature, Complexity feature, Fuzzy membership function, Medicine, Science
Description: Abstract Feature extraction based on EEG signals and construction of classification models using machine learning methods are key to intelligent assisted diagnosis of brain diseases (such as stroke classification). However, the quality of the extracted features directly affects the classification performance. This study proposes a multi-dimensional feature extraction method based on autocorrelation and complexity theory. It introduces an improved multifractal detrended fluctuation analysis (MFDFA) algorithm based on optimized empirical mode decomposition to extract high-quality autocorrelation features. In addition, we find that the ratio of fuzzy entropy between high-frequency band and low-frequency band of cerebral infarction signals is significantly lower than that of cerebral hemorrhage signals. On this basis, we propose a new complexity feature - fuzzy asymmetric index (FAI) based on constant Gaussian membership function. The study then integrates hierarchical fuzzy entropy, asymmetric entropy, and FAI to obtain complex fusion features. These extracted and fused features demonstrate excellent classification performance for differentiating cerebral hemorrhage and cerebral infarction. Using the random forest algorithm with a constant Gaussian membership function, the classification achieves an accuracy of 99.33%, precision of 100%, sensitivity of 98.57%, specificity of 100%, F1-score of 99.23%, and MCC of 98.73%. The proposed multi-dimensional features, combining autocorrelation and complexity characteristics, perform remarkably well in the classification of stroke EEG signals.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-04756-0
Access URL: https://doaj.org/article/190ed4de253c4a1384e43ba3d08f2094
Accession Number: edsdoj.190ed4de253c4a1384e43ba3d08f2094
Database: Directory of Open Access Journals
Description
Abstract:Abstract Feature extraction based on EEG signals and construction of classification models using machine learning methods are key to intelligent assisted diagnosis of brain diseases (such as stroke classification). However, the quality of the extracted features directly affects the classification performance. This study proposes a multi-dimensional feature extraction method based on autocorrelation and complexity theory. It introduces an improved multifractal detrended fluctuation analysis (MFDFA) algorithm based on optimized empirical mode decomposition to extract high-quality autocorrelation features. In addition, we find that the ratio of fuzzy entropy between high-frequency band and low-frequency band of cerebral infarction signals is significantly lower than that of cerebral hemorrhage signals. On this basis, we propose a new complexity feature - fuzzy asymmetric index (FAI) based on constant Gaussian membership function. The study then integrates hierarchical fuzzy entropy, asymmetric entropy, and FAI to obtain complex fusion features. These extracted and fused features demonstrate excellent classification performance for differentiating cerebral hemorrhage and cerebral infarction. Using the random forest algorithm with a constant Gaussian membership function, the classification achieves an accuracy of 99.33%, precision of 100%, sensitivity of 98.57%, specificity of 100%, F1-score of 99.23%, and MCC of 98.73%. The proposed multi-dimensional features, combining autocorrelation and complexity characteristics, perform remarkably well in the classification of stroke EEG signals.
ISSN:20452322
DOI:10.1038/s41598-025-04756-0