Providing Intelligent Software to Diagnose the Type and Severity of Mental Disorders Based on QEEG: A Comparative Study between the Statistical Method and the Intelligent Method

Background: Identifying mental disorder biomarkers is one of the leadinggoals of the clinical science.Objectives: This study aimed to provide an artificial intelligence based solutionand software program to diagnose the type and severity of mental disordersaccording to the quantitative electroencephalogram (QEEG) of patients.Materials and Methods: The QEEG data collected from 45 patients addictedto one of the substances (crystal-glass methamphetamine [n=15], tramadol[n=15], heroin/opium [n=15]) and 15 healthy people. They were entered intoSPSS version 20 and analyzed by Discriminant Analysis (DA) function andsimultaneously used as the Training Group of the artificial neural network(ANN) of the diagnosis software. In order to test and validate the software, inthe following, QEEG was also recorded from the remaining 60 subjects (45addicted and 15 healthy people).Results: The results obtained from the software were 0.836, 0.884, 7.21, 0.19,0.712, and 0.890, respectively. Meanwhile, the values of these parameters forDA were 0.677, 0.66, 1.99, 0.49, 0.363, and 0.739, respectively. The results ofthe software significantly improved the diagnosis. Totally nine discriminantfunctions were obtained for the frontal, parietal and central lobes was obtainedaccording to the delta, Theta, Alpha and Beta variables.Conclusion: As a result, intelligent diagnosis software provided can be usedwith a high sensitivity and great specificity rather than Paper-Pencil tests foraccurate diagnosis of the type of disorder and expressing its severity at aconfidence level that is scientifically computed and displayed.

Providing Intelligent Software to Diagnose the Type and Severity of Mental Disorders Based on QEEG: A Comparative Study between the Statistical Method and the Intelligent Method Keywords:

Artificial Intelligence , Diagnosis , Electroencephalography , Neurolinguistic Programming , Discriminant Analysis