Combined psychophysiological assessment of ADHD: a pilot study of Bayesian probability approach illustrated by appraisal of ADHD in female college students.
Manifestations of ADHD are observed at both psychological and physiological levels and assessed via various psychometric, EEG, and imaging tests. However, no test is 100% accurate in its assessment of ADHD. This study introduces a stochastic assessment combining psychometric tests with previously reported (Consistency Index) and newly developed (Alpha Blockade Index) EEG-based physiological markers of ADHD. The assessment utilizes classical Bayesian inference to refine after each step the probability of ADHD of each individual. In a pilot study involving six college females with ADHD and six matched controls, the assessment achieved correct classification for all ADHD and non-ADHD participants. In comparison, the classification of ADHD versus non-ADHD participants was < 85% for any one of the tests separately. The procedure significantly improved the score separation between ADHD versus non-ADHD groups. The final average probabilities for ADHD were 76% for the ADHD group and 8% for the control group. These probabilities correlated (r = .87) with the Brown ADD scale and (r = .84) with the ADHD-Symptom Inventory used for the screening of the participants. We conclude that, although each separate test was not completely accurate, a combination of several tests classified correctly all ADHD and all non-ADHD participants. The application of the proposed assessment is not limited to the specific tests used in this study--the assessment represents a general paradigm capable of accommodating a variety of ADHD tests into a single diagnostic assessment.
Robeva
Raina
R
Department of Mathematical Sciences, Sweet Briar College, Sweet Briar, Virginia 24595, USA. robeva@sbc.edu
Penberthy
Jennifer Kim
JK
Loboschefski
Tim
T
Cox
Daniel
D
Kovatchev
Boris
B
D000328:Adult; D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis* / Q000503:physiopathology; D001499:Bayes Theorem; D003937:Diagnosis, Differential; D004569:Electroencephalography; D005260:Female; D006801:Humans; D010865:Pilot Projects; D011569:Psychiatric Status Rating Scales; D011594:Psychometrics; D012680:Sensitivity and Specificity