Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis

• URL: http://arxiv.org/abs/2301.01588v1
• Date: Wed, 23 Nov 2022 00:28:41 GMT
• Title: Power Spectral Density-Based Resting-State EEG Classification of First-Episode Psychosis
• Authors: Sadi Md. Redwan, Md Palash Uddin, Anwaar Ulhaq, and Muhammad Imran Sharif
• Abstract summary: We show 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) A comprehensive discussion of our preprocessing methods for PSD analysis and a detailed comparison of different models are included in this paper.
• Score: 1.3416169841532526
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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