Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection
Abstract Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and accurate diagnosis of schizophrenia remains a challenge due to the disorder’s complex nature and the limitations of state-of-the-art techniq...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-05028-7 |
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| author | Mohammad Abrar Abdu Salam Ahmed Albugmi Fahad Al-otaibi Farhan Amin Isabel de la Torre Thania Candelaria Chio Montero Perla Araceli Arroyo Gala |
| author_facet | Mohammad Abrar Abdu Salam Ahmed Albugmi Fahad Al-otaibi Farhan Amin Isabel de la Torre Thania Candelaria Chio Montero Perla Araceli Arroyo Gala |
| author_sort | Mohammad Abrar |
| collection | DOAJ |
| description | Abstract Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and accurate diagnosis of schizophrenia remains a challenge due to the disorder’s complex nature and the limitations of state-of-the-art techniques. It is evident from the literature that electroencephalogram (EEG) signals provide valuable insights into brain activity, but their high dimensionality and complexity pose remain key challenges. Thus, our research introduces a novel approach by integrating the multichannel EGG, Crossover-Boosted Archimedes Optimization Algorithm (CAOA), and Rough Set Theory (RST) for schizophrenia detection. It is a four-stage model. In the first stage, Raw EGG data is collected. The data is passed to the next stage, which is called data preprocessing. This is used for artifact removal, band-pass filtering, and data normalization. The preprocessed data passed to the next stage. In the feature extraction stage, feature selection is performed using CAOA. In addition, classification is performed using a Support Vector Machine (SVM) based on features extracted through Multivariate Empirical Mode Function (MEMF) and entropy measures. The data interpretation stage displays the results to the end user using the data interpretation stage. We experimented and tested our proposed model using real EEG datasets. The simulation results prove that the proposed model achieved an average accuracy of 94.9%, sensitivity of 93.9%, specificity of 96.4%, and precision of 92.7%. Thus, our proposed model demonstrates significant improvements over state-of-the-art methods. In addition, the integration of CAOA and RST effectively addresses the challenges of high-dimensional EEG data, helps optimize the feature selection process, and increases accuracy. In future work, we suggest incorporating large-size datasets that include more diverse patient groups and refining the model with advanced machine-learning models and techniques. |
| format | Article |
| id | doaj-art-a3db3e075e7e4d4382ea2b4e68335a97 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a3db3e075e7e4d4382ea2b4e68335a972025-08-20T04:01:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-05028-7Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selectionMohammad Abrar0Abdu Salam1Ahmed Albugmi2Fahad Al-otaibi3Farhan Amin4Isabel de la Torre5Thania Candelaria Chio Montero6Perla Araceli Arroyo Gala7Faculty of Computer Studies, Arab Open UniversityDepartment of Computer Science, Abdul Wali Khan University MardanComputer and IT Department, The Applied College King Abdulaziz UniversityFaculty of Computing and Information Technology, King Abdulaziz UniversitySchool of Computer Science and Engineering, Yeungnam UniversityDepartment of Signal Theory and Communications, University of ValladolidUniversidad de La RomanaUniversidad Internacional IberoamericanaAbstract Schizophrenia is a mental disorder characterized by hallucinations, delusions, disorganized thinking and behavior, and inappropriate affect. Early and accurate diagnosis of schizophrenia remains a challenge due to the disorder’s complex nature and the limitations of state-of-the-art techniques. It is evident from the literature that electroencephalogram (EEG) signals provide valuable insights into brain activity, but their high dimensionality and complexity pose remain key challenges. Thus, our research introduces a novel approach by integrating the multichannel EGG, Crossover-Boosted Archimedes Optimization Algorithm (CAOA), and Rough Set Theory (RST) for schizophrenia detection. It is a four-stage model. In the first stage, Raw EGG data is collected. The data is passed to the next stage, which is called data preprocessing. This is used for artifact removal, band-pass filtering, and data normalization. The preprocessed data passed to the next stage. In the feature extraction stage, feature selection is performed using CAOA. In addition, classification is performed using a Support Vector Machine (SVM) based on features extracted through Multivariate Empirical Mode Function (MEMF) and entropy measures. The data interpretation stage displays the results to the end user using the data interpretation stage. We experimented and tested our proposed model using real EEG datasets. The simulation results prove that the proposed model achieved an average accuracy of 94.9%, sensitivity of 93.9%, specificity of 96.4%, and precision of 92.7%. Thus, our proposed model demonstrates significant improvements over state-of-the-art methods. In addition, the integration of CAOA and RST effectively addresses the challenges of high-dimensional EEG data, helps optimize the feature selection process, and increases accuracy. In future work, we suggest incorporating large-size datasets that include more diverse patient groups and refining the model with advanced machine-learning models and techniques.https://doi.org/10.1038/s41598-025-05028-7EEG dataSchizophrenia detectionArtificial intelligenceMachine learningBig dataDeep learning |
| spellingShingle | Mohammad Abrar Abdu Salam Ahmed Albugmi Fahad Al-otaibi Farhan Amin Isabel de la Torre Thania Candelaria Chio Montero Perla Araceli Arroyo Gala Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection Scientific Reports EEG data Schizophrenia detection Artificial intelligence Machine learning Big data Deep learning |
| title | Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection |
| title_full | Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection |
| title_fullStr | Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection |
| title_full_unstemmed | Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection |
| title_short | Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection |
| title_sort | enhanced schizophrenia detection using multichannel eeg and caoa rst based feature selection |
| topic | EEG data Schizophrenia detection Artificial intelligence Machine learning Big data Deep learning |
| url | https://doi.org/10.1038/s41598-025-05028-7 |
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