Psychogenic tremor disorders identified using tree-based statistical algorithms and quantitative tremor analysis.
Detecting psychogenic tremors (PsychT) is often challenging. As there are no laboratory investigations or imaging techniques that can confirm the diagnosis, PsychT is identified on a clinical basis. We present a tree-based statistical algorithm derived from quantitative computerized tremor recordings as a novel method to help in the recognition of PsychT. The goal of this study was to show that objective data from computerized tremor recordings, when processed through a tree-based statistical algorithm, can be used to determine whether a patient can be classified as having PsychT.
Piboolnurak
Panida
P
Department of Neurology, Clinical Motor Physiology Laboratory, Columbia University Medical Center, New York, New York, USA.
Rothey
Natalia
N
Ahmed
Anwar
A
Ford
Blair
B
Yu
Qiping
Q
Xu
Dong
D
Pullman
Seth L
SL
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D003937:Diagnosis, Differential; D004576:Electromyography / Q000379:methods; D005260:Female; D006801:Humans; D007700:Kinetics; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009460:Neurologic Examination; D011237:Predictive Value of Tests; D011602:Psychophysiologic Disorders / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014202:Tremor / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology