A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings

dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorSobahi, Nebras
dc.contributor.authorÇakar, Hakan
dc.contributor.authorArı, Berna
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorŞengür, Abdulkadir
dc.date.accessioned2022-04-25T11:15:55Z
dc.date.available2022-04-25T11:15:55Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractMachine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods. IEEEen_US
dc.identifier.citationSobahi, N., Ari, B., Cakar, H., Alcin, O. F., & Sengur, A. (2022). A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings. IEEE Sensors Journal, 22(8), 7913-7919.en_US
dc.identifier.doi10.1109/JSEN.2022.3151465
dc.identifier.endpage7919en_US
dc.identifier.issn1530437Xen_US
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85124817235en_US
dc.identifier.startpage7913en_US
dc.identifier.urihttps://doi.org/10.1109/JSEN.2022.3151465
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1037
dc.identifier.volume22en_US
dc.identifier.wosWOS:000803129500053en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAlçin, Ömer Faruk
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofIEEE Sensors Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSchizophrenia detectionen_US
dc.subjectEEG rhythmsen_US
dc.subjectCNNen_US
dc.subjectfine-tuningen_US
dc.titleA New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordingsen_US
dc.typeArticleen_US

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