Diagnosis of schizophrenia based on transformation from EEG sub-bands to the image with deep learning architecture
Author:
Funder
Bilimsel Araştırma Projeleri Birimi, Mardin Üniversitesi
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-023-09492-z.pdf
Reference35 articles.
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2. Alimardani F, Boostani R (2018) DB-FFR: a modified feature selection algorithm to improve discrimination rate between bipolar mood disorder (BMD) and schizophrenic patients. Iran J Sci Technol Trans Electr Eng 42(3):251–260. https://doi.org/10.1007/s40998-018-0060-x
3. Alimardani F, Cho JH, Boostani R, Hwang HJ (2018) Classification of bipolar disorder and schizophrenia using steady-state visual evoked potential based features. IEEE Access 6:40379–40388. https://doi.org/10.1109/ACCESS.2018.2854555
4. Aslan Z, Akin M (2020) Automatic detection of schizophrenia by applying deep learning over spectrogram images of EEG signals. Traitement Du Signal 37(2):235–244. https://doi.org/10.18280/ts.370209
5. Barry J (2019) A deep learning approach to diagnosing schizophrenia. Electron Theses Dissertations. 6300. https://stars.library.ucf.edu/etd/6300
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