EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection
Schizophrenia is a chronic and severe mental illness that significantly impacts the daily lives and work of those affected. Unfortunately, schizophrenia with negative symptoms often gets misdiagnosed, relying heavily on the clinician’s experience. There is a pressing need to develop an objective and...
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2811.pdf |
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| author | Alanoud Al Mazroa Majdy M. Eltahir Shouki A. Ebad Faiz Abdullah Alotaibi Venkatachalam K Jaehyuk Cho |
| author_facet | Alanoud Al Mazroa Majdy M. Eltahir Shouki A. Ebad Faiz Abdullah Alotaibi Venkatachalam K Jaehyuk Cho |
| author_sort | Alanoud Al Mazroa |
| collection | DOAJ |
| description | Schizophrenia is a chronic and severe mental illness that significantly impacts the daily lives and work of those affected. Unfortunately, schizophrenia with negative symptoms often gets misdiagnosed, relying heavily on the clinician’s experience. There is a pressing need to develop an objective and effective diagnostic method for this specific type of schizophrenia. This paper proposes a new deep-learning method called Cascaded Atrous Convolutional Network with Adaptive Weight Fusion (CA-AWFM) for classifying schizophrenia from electroencephalogram (EEG) data that combines cascaded networks with atrous convolutions and an adaptive weight fusion module (AWFM). This is because schizophrenia involves intricate and subtle brain wave patterns that make it difficult to detect the disorder from EEG signals. As such, our model uses an “atrous” convolution operation to extract multi-scale temporal information and a cascade network structure that progressively improves the attribute representations across layers. For classification purposes, AWFM enables our model to modify the importance of features dynamically. We evaluated our technique using a publicly available dataset of EEG recordings acquired from patients who have schizophrenia and everyday individuals. The proposed model has significantly outperformed existing methods with a 99.5% accuracy rate. With the help of atrous convolutions, local and global dependencies within the EEGs can be effectively modeled in this way. At the same time, AWFM makes flexible prioritization of characteristics possible for improved classification performance. With such impressive figures achieved, it can be concluded that our approach should be considered as accurate enough for routine clinical use in identifying schizophrenic patients early on so they can receive intervention measures on time or when diagnosed late, then dealt with appropriately. |
| format | Article |
| id | doaj-art-62efeda5be2d40919ed5c655ff346e4e |
| institution | DOAJ |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ Computer Science |
| spelling | doaj-art-62efeda5be2d40919ed5c655ff346e4e2025-08-20T02:58:55ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e281110.7717/peerj-cs.2811EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selectionAlanoud Al Mazroa0Majdy M. Eltahir1Shouki A. Ebad2Faiz Abdullah Alotaibi3Venkatachalam K4Jaehyuk Cho5Department of Information Systems, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Systems, King Khalid University, Abha, Saudi ArabiaCenter for Scientific Research and Entrepreneurship, Northern Border University, Arar, Saudi ArabiaDepartment of Information Science, King Saud University, Riyadh, Saudi ArabiaDepartment of Software Engineering, Jeonbuk National University, Jeonju, Republic of KoreaDepartment of Software Engineering and Division of Electronics & Information Engineering, Jeonbuk National University, Jeonju, Republic of KoreaSchizophrenia is a chronic and severe mental illness that significantly impacts the daily lives and work of those affected. Unfortunately, schizophrenia with negative symptoms often gets misdiagnosed, relying heavily on the clinician’s experience. There is a pressing need to develop an objective and effective diagnostic method for this specific type of schizophrenia. This paper proposes a new deep-learning method called Cascaded Atrous Convolutional Network with Adaptive Weight Fusion (CA-AWFM) for classifying schizophrenia from electroencephalogram (EEG) data that combines cascaded networks with atrous convolutions and an adaptive weight fusion module (AWFM). This is because schizophrenia involves intricate and subtle brain wave patterns that make it difficult to detect the disorder from EEG signals. As such, our model uses an “atrous” convolution operation to extract multi-scale temporal information and a cascade network structure that progressively improves the attribute representations across layers. For classification purposes, AWFM enables our model to modify the importance of features dynamically. We evaluated our technique using a publicly available dataset of EEG recordings acquired from patients who have schizophrenia and everyday individuals. The proposed model has significantly outperformed existing methods with a 99.5% accuracy rate. With the help of atrous convolutions, local and global dependencies within the EEGs can be effectively modeled in this way. At the same time, AWFM makes flexible prioritization of characteristics possible for improved classification performance. With such impressive figures achieved, it can be concluded that our approach should be considered as accurate enough for routine clinical use in identifying schizophrenic patients early on so they can receive intervention measures on time or when diagnosed late, then dealt with appropriately.https://peerj.com/articles/cs-2811.pdfSchizophrenia detectionEEGDeep learningAtrous convolutionAdaptive weight fusion moduleMental health diagnosis |
| spellingShingle | Alanoud Al Mazroa Majdy M. Eltahir Shouki A. Ebad Faiz Abdullah Alotaibi Venkatachalam K Jaehyuk Cho EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection PeerJ Computer Science Schizophrenia detection EEG Deep learning Atrous convolution Adaptive weight fusion module Mental health diagnosis |
| title | EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection |
| title_full | EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection |
| title_fullStr | EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection |
| title_full_unstemmed | EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection |
| title_short | EEG-based schizophrenia diagnosis using deep learning with multi-scale and adaptive feature selection |
| title_sort | eeg based schizophrenia diagnosis using deep learning with multi scale and adaptive feature selection |
| topic | Schizophrenia detection EEG Deep learning Atrous convolution Adaptive weight fusion module Mental health diagnosis |
| url | https://peerj.com/articles/cs-2811.pdf |
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