Classification of fNIRS Data Using Deep Learning for Bipolar Disorder Detection

Evgin, Haluk Barkin
Babacan, Oguzhan
Ulusoy, İlkay
Hosgoren, Yasemin
Kusman, Adnan
Sayar, Damla
Baskak, Bora
Ozguven, Halise Devrimci
With the use of ecologically validated tools more applicable measurements can be obtained, especially of individuals who have psychological disorders. Functional Near-Infrared Spectroscopy (fNIRS) is a neural imaging method that comes into prominence for imaging patients who have psychological disorders. It is a desired method because of its feasibility, high resolution in time and its partial resistance to head movements. Following the developments in the artificial intelligence, individuals' medical data obtained from various methods are started to be used in neural networks to classify various health conditions. In this research, 1 dimensional time domain data of fNIRS, which is acquired during prepared tasks, are used to train a neural network for the diagnosis of a common mood disorder, the Bipolar Disorder. With the classification of this data, the distinguishability of ill subjects from healthy subjects is investigated by using a 1 dimensional Convolutional Neural Network (CNN), which is a feed-forward deep neural network. By means of the obtained results, it is observed that the Bipolar Disorder can be classified even during the remission period.


Deep learning for the classification of bipolar disorder using fNIRS measurements
Evgin, Haluk Barkın; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2021-2-3)
Functional Near-Infrared Spectroscopy (fNIRS) is a neural imaging method that is proved to be prominent in the classification of psychiatric disorders, and assertive accuracy results are being obtained using fNIRS. High temporal resolution, feasibility, and partial endurance to head movements are the traits that are highlighting fNIRS among other imaging methods. fNIRS data is a one dimensional multi-channeled time series. In this thesis, bipolar disorder is classified using some state of the art deep learn...
EEG Classification based on Image Configuration in Social Anxiety Disorder
Mokatren, Lubna Shibly; Ansari, Rashid; Cetin, Ahmet Enis; Leow, Alex D.; Ajilore, Olusola; Klumpp, Heide; Yarman Vural, Fatoş Tunay (2019-01-01)
The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting ...
Evaluation of Whole Genome Association Study Data in Bipolar Disorders: Potential Novel SNPs and Genes
CENGİZ HAN, Açıkel; Aydın Son, Yeşim; ÇELİK, Cemil; RECEP, Tütüncü (2015-03-01)
Objective: As a result of studies of multifactorial conditions, genetic, physiological and environmental factors, the overall heritability of bipolar disorders has been estimated to be up to 70%. In this study, an analysis of genome-wide association study data using data mining algorithms has revealed single-nucleotide polymorphisms that may be the basis for the molecular etiology of bipolar disorders.
Ben-Tovim Walker Beden Tutum Ölçeği (BTWÖ)’nin Türkçe Formunun Psikometrik Özelliklerinin İncelenmesi
Mahperi ULUYOL, Fatma; BARIŞKIN, Elif (Orta Doğu Teknik Üniversitesi (Ankara, Turkey), 2020-2-28)
Beden memnuniyetsizliğinin yeme bozukluğu gelişme riskini önemli oranda arttırması, bu yapıyı yordayacak güçlü ölçüm araçlarına ihtiyaç doğurmaktadır. Bu çalışmanın amacı da Ben-Tovim Walker Beden Tutum Ölçeği (Ben-Tovim Walker Body Attitudes Questionnaire)’nin Türkiye’deki genç kadın örneklemindeki psikometrik özelliklerinin incelenmesidir. Bu doğrultuda BTWÖ, Yeme Tutum Testi (YTT), Benlik Saygısı Ölçeği (BSÖ) ve Vücut Algısı Ölçeği (VAÖ) 599 üniversite öğrencisi kadına uygulanmıştır. Ölçeğin yapı geçerli...
Classification of fMRI Data by Using Clustering
Moğultay, Hazal; Yarman Vural, Fatoş Tunay (2015-05-19)
Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional bra...
Citation Formats
H. B. Evgin et al., “Classification of fNIRS Data Using Deep Learning for Bipolar Disorder Detection,” 2019, Accessed: 00, 2020. [Online]. Available: