Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.
Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhoo...
| Published in: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 19 |
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| Main Authors: | , , , |
| Format: | pictorial research tables/charts tracings Journal Article |
| Published: |
Springer Nature
3/5/2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175896698&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175896698 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 3/5/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175896698 175896698 175896698 10.1007/s10916-024-02048-0 175896698 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records. aug: au: Aksoy, Gamzepelin Cattan, Grégoire Chakraborty, Subrata Karabatak, Murat affil: https://ror.org/05teb7b63 Department of Software Engineering, Firat University, Elazig, Türkiye sug: subj: Decision Support Systems, Clinical Methods Machine Learning Utilization Schizophrenia Electroencephalography Human Factor Analysis Funding Source Logistic Regression Male Adolescence Magnetic Resonance Imaging Adolescent: 13-18 years Male ab: Schizophrenia is a serious chronic mental disorder that significantly affects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specific treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using different qubit numbers and different circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classification of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be effectively utilized in the field of healthcare. pubtype: Academic Journal doctype: pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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