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Updated: Jul 7, 2025
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach
1Department of Biomedical Engineering, Faculty of Engineering, Erciyes University, Kayseri 38280, Turkey.
Insights
This study introduces a novel EEG analysis method using circulant spectrum analysis (ciSSA) for early detection of first-episode psychosis (FEP). The approach achieved high accuracy, demonstrating its potential for improved mental health diagnostics.
Area of Science:
- Neuroscience
- Psychiatry
- Signal Processing
Background:
- First-episode psychosis (FEP) signifies the onset of severe psychiatric disorders, making early diagnosis crucial for effective intervention and better patient outcomes.
- Accurate and timely diagnosis of FEP presents a significant challenge in mental healthcare.
- Electroencephalography (EEG) signals offer a potential avenue for objective diagnostic markers in psychiatric conditions.
Purpose of the Study:
- To develop and validate a novel, high-performance classification method for the early diagnosis of FEP.
- To investigate the utility of circulant spectrum analysis (ciSSA) sub-band signals derived from EEG for FEP classification.
- To identify and leverage significant features from EEG signals for machine learning-based FEP detection.
Main Methods:
- EEG signals were analyzed using circulant spectrum analysis (ciSSA) to extract sub-band features.
- The LASSO method was employed for feature selection, focusing on entropy, frequency, and statistical characteristics from ciSSA sub-bands and their combinations.
- Machine learning models, including ensemble methods, Support Vector Machine (SVM), and Artificial Neural Network (ANN), were utilized for classification.
Main Results:
- The hybrid features from EEG ciSSA sub-bands combined with the SVM classifier yielded exceptional performance.
- Key performance metrics included an Area Under the Curve (AUC) of 0.9893, 96.23% accuracy, 0.966 sensitivity, 0.956 specificity, 0.9667 precision, and 0.9666 F1 score.
- The findings underscore the effectiveness of the ciSSA-based approach for classifying FEP using EEG data.
Conclusions:
- The ciSSA-based method demonstrates significant potential as an effective tool for the early and accurate classification of FEP from EEG signals.
- This approach offers a promising advancement in objective diagnostic strategies for first-episode psychosis.
- Further research can explore the clinical integration of this EEG analysis technique for improved mental health diagnostics.
Abstract:
First-episode psychosis (FEP) typically marks the onset of severe psychiatric disorders and represents a critical period in the field of mental health. The early diagnosis of this condition is essential for timely intervention and improved clinical outcomes. In this study, the classification of FEP was investigated using the analysis of electroencephalography (EEG) signals and circulant spectrum analysis (ciSSA) sub-band signals. FEP poses a significant diagnostic challenge in the realm of mental health, and it is aimed at introducing a novel and effective approach for early diagnosis. To achieve this, the LASSO method was utilized to select the most significant features derived from entropy, frequency, and statistical-based characteristics obtained from ciSSA sub-band signals, as well as their hybrid combinations. Subsequently, a high-performance classification model has been developed using machine learning techniques, including ensemble, support vector machine (SVM), and artificial neural network (ANN) methods. The results of this study demonstrated that the hybrid features extracted from EEG signals' ciSSA sub-bands, in combination with the SVM method, achieved a high level of performance, with an area under curve (AUC) of 0.9893, an accuracy of 96.23%, a sensitivity of 0.966, a specificity of 0.956, a precision of 0.9667, and an F1 score of 0.9666. This has revealed the effectiveness of the ciSSA-based method for classifying FEP from EEG signals.
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