Using time domain characteristics to discriminate physiologic and parkinsonian tremors.
Tremor amplitude and frequency do not always clearly differentiate subjects with particular pathologies from control subjects or from subjects with other pathologies, especially in early stages of a disease. For patients with early stages of Parkinson's disease (PD) the discriminative power of amplitude was compared with that of other time domain characteristics of tremor recordings that are probably not evident clinically. Postural tremor with and without visual feedback and rest tremor were recorded in both hands of a group of patients with Parkinson's disease (n = 21) and a group of healthy control subjects (n = 30) using displacement lasers. Velocity and acceleration data were derived from displacement data. Twelve time domain characteristics were calculated on each recording and the discriminating power of each was evaluated using the worse hand in each case. Postural tremor with no visual feedback separates the two groups of subjects most efficiently, especially in velocity and acceleration. Tremor in Parkinson's disease (in comparison to normal physiologic tremor) has a specific morphology, has a distinctive histogram, is more periodic, and contains indications of nonlinearity in the underlying dynamics. There may also be greater difference in amplitude between the two hands and time asymmetry in tremor of patients with PD. A series of finger flexions seems to enhance normal tremor but not tremor in PD and may thus aid in discrimination. Discrimination of tremor attributable to PD from normal physiologic tremor can be enhanced by measuring time domain characteristics subtler than amplitude, particularly when amplitude itself is not large. Tremor measurement should not be limited to acceleration data because some information is more visible in other variables.
Edwards
R
R
Department of Mathematics and Statistics, University of Victoria, British Columbia, Canada.
Beuter
A
A
D000368:Aged; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005246:Feedback / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis* / Q000503:physiopathology*; D010507:Periodicity; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014202:Tremor / Q000145:classification* / Q000209:etiology / Q000503:physiopathology*