Power spectral density-based resting-state EEG classification of first-episode psychosis
Article
Article Title | Power spectral density-based resting-state EEG classification of first-episode psychosis |
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ERA Journal ID | 201487 |
Article Category | Article |
Authors | Redwan, Sadi Md., Uddin, Md Palash, Ulhaq, Anwaar, Sharif, Muhammad Imran and Krishnamoorthy, Govind |
Journal Title | Scientific Reports |
Journal Citation | 14 |
Article Number | 15154 |
Number of Pages | 12 |
Year | 2024 |
Publisher | Nature Publishing Group |
Place of Publication | United Kingdom |
ISSN | 2045-2322 |
Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-024-66110-0 |
Web Address (URL) | https://www.nature.com/articles/s41598-024-66110-0 |
Abstract | Historically, the analysis of stimulus-dependent time–frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The abnormal oscillations in high-frequency waves associated with psychotic disorders during sensory and cognitive tasks have been studied many times. However, any significant dissimilarity in the resting-state low-frequency bands is yet to be established. Spectral analysis of the alpha and delta band waves shows the effectiveness of stimulus-independent EEG in identifying the abnormal activity patterns of pathological brains. A generalized model incorporating multiple frequency bands should be more efficient in associating potential EEG biomarkers with first-episode psychosis (FEP), leading to an accurate diagnosis. We explore multiple machine-learning methods, including random-forest, support vector machine, and Gaussian process classifier (GPC), to demonstrate the practicality of resting-state power spectral density (PSD) to distinguish patients of FEP from healthy controls. A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper. The GPC model outperforms the other models with a specificity of 95.78% to show that PSD can be used as an effective feature extraction technique for analyzing and classifying resting-state EEG signals of psychiatric disorders. |
Keywords | First episode psychosis, EEG, PSD, GPC, machine-learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 4611. Machine learning |
420313. Mental health services | |
Byline Affiliations | University of Rajshahi, Bangladesh |
Hajee Mohammad Danesh Science and Technology University, Bangladesh | |
Central Queensland University | |
Kansas State University, United States | |
University of Southern Queensland |
https://research.usq.edu.au/item/z7yyy/power-spectral-density-based-resting-state-eeg-classification-of-first-episode-psychosis
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