title: An MLP-based model for identifying qEEG in depression.
abstract: Manual differentiation of electroencephalography (EEG) paper recordings in cases of depression is not very helpful. So, a Multilayer Perceptron (MLP) has been used to differentiate the EEG power density spectra (qEEG) in the wakeful state from animals (control, exercised and depressed). The qEEG ranging from 1 to 30 Hz, at 1 Hz increments (30 input features) and also a slow, medium and fast activity (represented by three ranges of frequencies at the input) were used. After training with depressed and control qEEG only, the MLP has been found to distinguish successfully between the normal and the depressed rats in more than 80% of the cases, identifying, in the process, most of the exercised groups' EEG as normal. The reduction in the dimension of input features from 30 individual frequencies to 3 frequency bands has produced similar results. The rules generated for making such distinctions have been found to be similar to the clinical views.
authors.affiliation: Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India.
authors.forename: S
authors.initials: S
authors.lastname: Mitra
authors.forename: S N
authors.initials: SN
authors.lastname: Sarbadhikari
authors.forename: S K
authors.initials: SK
authors.lastname: Pal
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D003863:Depression / Q000175:diagnosis* / Q000209:etiology / Q000503:physiopathology; D004569:Electroencephalography; D008297:Male; D016571:Neural Networks, Computer; D051381:Rats; D012815:Signal Processing, Computer-Assisted; D013312:Stress, Physiological / Q000150:complications / Q000503:physiopathology