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.
Citation Formats
H. B. Evgin et al., “Classification of fNIRS Data Using Deep Learning for Bipolar Disorder Detection,” 2019, Accessed: 00, 2020. [Online]. Available: