Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals
Background/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five dis...
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MDPI AG
2025-01-01
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| Online Access: | https://www.mdpi.com/2076-3425/15/1/68 |
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| author | Arezoo Sanati Fahandari Sara Moshiryan Ateke Goshvarpour |
| author_facet | Arezoo Sanati Fahandari Sara Moshiryan Ateke Goshvarpour |
| author_sort | Arezoo Sanati Fahandari |
| collection | DOAJ |
| description | Background/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders. Methods: Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers. Results: The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. Conclusions: The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders. |
| format | Article |
| id | doaj-art-9a8934d2f74c4bd6b8f0659dc811e8ff |
| institution | Kabale University |
| issn | 2076-3425 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Brain Sciences |
| spelling | doaj-art-9a8934d2f74c4bd6b8f0659dc811e8ff2025-01-24T13:25:52ZengMDPI AGBrain Sciences2076-34252025-01-011516810.3390/brainsci15010068Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram SignalsArezoo Sanati Fahandari0Sara Moshiryan1Ateke Goshvarpour2Department of Biomedical Engineering, Imam Reza International University, Mashhad 91388-3186, IranDepartment of Biomedical Engineering, Imam Reza International University, Mashhad 91388-3186, IranDepartment of Biomedical Engineering, Imam Reza International University, Mashhad 91388-3186, IranBackground/Objectives: The classification of psychological disorders has gained significant importance due to recent advancements in signal processing techniques. Traditionally, research in this domain has focused primarily on binary classifications of disorders. This study aims to classify five distinct states, including one control group and four categories of psychological disorders. Methods: Our investigation will utilize algorithms based on Granger causality and local graph structures to improve classification accuracy. Feature extraction from connectivity matrices was performed using local structure graphs. The extracted features were subsequently classified employing K-Nearest Neighbors (KNN), Support Vector Machine (SVM), AdaBoost, and Naïve Bayes classifiers. Results: The KNN classifier demonstrated the highest accuracy in the gamma band for the depression category, achieving an accuracy of 89.36%, a sensitivity of 89.57%, an F1 score of 94.30%, and a precision of 99.90%. Furthermore, the SVM classifier surpassed the other machine learning algorithms when all features were integrated, attaining an accuracy of 89.06%, a sensitivity of 88.97%, an F1 score of 94.16%, and a precision of 100% for the discrimination of depression in the gamma band. Conclusions: The proposed methodology provides a novel approach for analyzing EEG signals and holds potential applications in the classification of psychological disorders.https://www.mdpi.com/2076-3425/15/1/68Alzheimer’s diseasedepressionmild cognitive impairmentschizophreniaGranger causalitylocal graph structures |
| spellingShingle | Arezoo Sanati Fahandari Sara Moshiryan Ateke Goshvarpour Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals Brain Sciences Alzheimer’s disease depression mild cognitive impairment schizophrenia Granger causality local graph structures |
| title | Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals |
| title_full | Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals |
| title_fullStr | Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals |
| title_full_unstemmed | Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals |
| title_short | Diagnosis of Cognitive and Mental Disorders: A New Approach Based on Spectral–Spatiotemporal Analysis and Local Graph Structures of Electroencephalogram Signals |
| title_sort | diagnosis of cognitive and mental disorders a new approach based on spectral spatiotemporal analysis and local graph structures of electroencephalogram signals |
| topic | Alzheimer’s disease depression mild cognitive impairment schizophrenia Granger causality local graph structures |
| url | https://www.mdpi.com/2076-3425/15/1/68 |
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