دورية أكاديمية
Power spectral density-based resting-state EEG classification of first-episode psychosis
| العنوان: | Power spectral density-based resting-state EEG classification of first-episode psychosis |
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| المؤلفون: | Sadi Md. Redwan, Md Palash Uddin, Anwaar Ulhaq, Muhammad Imran Sharif, Govind Krishnamoorthy |
| المصدر: | Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024) |
| بيانات النشر: | Nature Portfolio, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | First-episode psychosis, EEG, PSD, GPC, Machine-learning, Medicine, Science |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-024-66110-0 |
| URL الوصول: | https://doaj.org/article/4e86c24e7d6b48279ad1733a9dc3ceed |
| رقم الأكسشن: | edsdoj.4e86c24e7d6b48279ad1733a9dc3ceed |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-024-66110-0 |