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Analysis of sleep deprivation effects based on nonlinear entropy features extracted from electroencephalogram signals
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Sina Shamekhi *   , Mohammad Fouladvand , Ali Ahmad Alipour |
| Sahand University of Technology , shamekhi@sut.ac.ir |
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Abstract: (3710 Views) |
| Nowadays, sleep deprivation is a pervasive problem that affects human physical and mental health. In this research, the effects of sleep deprivation on brain function and its diagnosis have been studied using electroencephalogram (EEG) signals recorded from 30 subjects after complete sleep and one day of sleep deprivation with open and closed eyes. Linear features like signal power and nonlinear features consisting of Shannon, Renyi, sample, and permutation entropies were extracted from signals. We used the PCA algorithm and Wilcoxon feature ranking method to extract the superior features and employed SVM, KNN, and a Decision tree to detect sleep-deprived cases. Brain maps of extracted features were plotted using the sLORETA algorithm to investigate the effects of sleep deprivation. Based on the results, the decision tree classifier with 100 superior selected features of Wilcoxon achieved the best performance with accuracy and precision of 99.0% and 99.8%, respectively. Also, comparing the results of linear and nonlinear features reveals the impressive role of the nonlinear features in the classification problem of this work. The maps of the features revealed noticeable changes in the level of attention, concentration, decision-making, and visual and movement activities. |
Article number: 3 |
| Keywords: Sleep deprivation, electroencephalogram, sLORETA, Analysis, classification, nonlinear |
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Full-Text [PDF 2484 kb]
(1277 Downloads)
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Type of Study: Research |
Subject:
Digital Signal Processing
Received: 2022/10/23 | Accepted: 2023/01/13 | Published: 2023/05/16
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