A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect

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dc.authoridORHAN BULUCU, Firat/0000-0003-4558-9667
dc.authorwosidORHAN BULUCU, Firat/HJH-7831-2023
dc.contributor.authorOrhanbulucu, Firat
dc.contributor.authorLatifoglu, Fatma
dc.contributor.authorBaydemir, Recep
dc.date.accessioned2024-08-04T20:54:27Z
dc.date.available2024-08-04T20:54:27Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractMigraine is a neurological disorder that is associated with severe headaches and seriously affects the lives of patients. Diagnosing Migraine Disease (MD) can be laborious and time-consuming for specialists. For this reason, systems that can assist specialists in the early diagnosis of MD are important. Although migraine is one of the most common neurological diseases, there are very few studies on the diagnosis of MD, especially electroencephalogram (EEG)-and deep learning (DL)-based studies. For this reason, in this study, a new system has been proposed for the early diagnosis of EEG- and DL-based MD. In the proposed study, EEG signals obtained from the resting state (R), visual stimulus (V), and auditory stimulus (A) from 18 migraine patients and 21 healthy control (HC) groups were used. By applying continuous wavelet transform (CWT) and short-time Fourier transform (STFT) methods to these EEG signals, scalogram-spectrogram images were obtained in the time-frequency (T-F) plane. Then, these images were applied as inputs in three different convolutional neural networks (CNN) architectures (AlexNet, ResNet50, SqueezeNet) that proposed deep convolutional neural network (DCNN) models and classification was performed. The results of the classification process were evaluated, taking into account accuracy (acc.), sensitivity (sens.), specificity (spec.), and performance criteria, and the performances of the preferred methods and models in this study were compared. In this way, the situation, method, and model that showed the most successful performance for the early diagnosis of MD were determined. Although the classification results are close to each other, the resting state, CWT method, and AlexNet classifier showed the most successful performance (Acc: 99.74%, Sens: 99.9%, Spec: 99.52%). We think that the results obtained in this study are promising for the early diagnosis of MD and can be of help to experts.en_US
dc.identifier.doi10.3390/diagnostics13111887
dc.identifier.issn2075-4418
dc.identifier.issue11en_US
dc.identifier.pmid37296739en_US
dc.identifier.scopus2-s2.0-85161754825en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.3390/diagnostics13111887
dc.identifier.urihttps://hdl.handle.net/11616/101406
dc.identifier.volume13en_US
dc.identifier.wosWOS:001005067600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherMdpien_US
dc.relation.ispartofDiagnosticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectconvolutional neural networksen_US
dc.subjectelectroencephalogramen_US
dc.subjectmigraine diseaseen_US
dc.subjectcontinuous wavelet transformen_US
dc.subjectshort-time Fourier transformen_US
dc.titleA New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effecten_US
dc.typeArticleen_US

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