Power spectral density-based resting-state EEG classification of first-episode psychosis

Article


Redwan, Sadi Md., Uddin, Md Palash, Ulhaq, Anwaar, Sharif, Muhammad Imran and Krishnamoorthy, Govind. 2024. "Power spectral density-based resting-state EEG classification of first-episode psychosis." Scientific Reports. 14. https://doi.org/10.1038/s41598-024-66110-0
Article Title

Power spectral density-based resting-state EEG classification of first-episode psychosis

ERA Journal ID201487
Article CategoryArticle
AuthorsRedwan, Sadi Md., Uddin, Md Palash, Ulhaq, Anwaar, Sharif, Muhammad Imran and Krishnamoorthy, Govind
Journal TitleScientific Reports
Journal Citation14
Article Number15154
Number of Pages12
Year2024
PublisherNature Publishing Group
Place of PublicationUnited Kingdom
ISSN2045-2322
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.

KeywordsFirst episode psychosis, EEG, PSD, GPC, machine-learning
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204611. Machine learning
420313. Mental health services
Byline AffiliationsUniversity of Rajshahi, Bangladesh
Hajee Mohammad Danesh Science and Technology University, Bangladesh
Central Queensland University
Kansas State University, United States
University of Southern Queensland
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