Kinematic evaluation in Parkinson's disease using a hand-held position transducer and computerized signal analysis.
BACKGROUND
The objective of this work was to develop a device for quantification of akinesia in Parkinson's disease, for the use in home monitoring of PD patients, as a part of home telecare programs. For this purpose a simple movement task is to be preferred, and the measurement devices must be small, lightweight, and easy to use, so patients may perform the measurements unattended. Another intended application was optimisation of the electrode position during implantations of neuromodulation systems for treatment of Parkinson.


METHODS
A hand held transducer was used to measure the position of the thumb while the patient repeatedly flexed and extended the thumb. The position data was sampled and stored on a personal computer with a plug in converter card and software. Measurements were performed on 15 PD patients and 6 age-matched controls. Signal analysis procedures were developed in order to automatically derive numerical parameters that quantify the movement performance. In order to select the most relevant parameters, they were correlated to Unified Parkinson Disease Rating Scale (UPDRS) motor scores (Spearman's rank, single sided, p < 0.05).


RESULTS
In reviews of the raw position signals the amplitude and frequency was found to be lower in patients than in controls. In patients the movement was frequently interrupted by short periods of hesitation. The calculated parameters of covered distance (correlation coefficient r = -0.63), hesitation (r = 0.64) and frequency (r = -0.6) were found to be most relevant, as they correlated best to the UPDRS hand pronation/supination score.


CONCLUSIONS
The equipment proved to be fast to setup and easy to use. The signal analysis methods provided meaningful numerical parameters for quantification of akinesia, represented in hand pronation/supination. These results suggest that the described methods may be useful for telemedicine and intraoperative use.
Sauermann
S
S
Department of Biomedical Engineering and Physics, University of Vienna, Vienna, Austria. stefan.sauermann@meduniwien.ac.at
Standhardt
H
H
Gerschlager
W
W
Lanmüller
H
H
Alesch
F
F
D000368:Aged; D001696:Biomechanical Phenomena / Q000295:instrumentation / Q000379:methods; D020820:Dyskinesias / Q000175:diagnosis / Q000503:physiopathology; D004568:Electrodiagnosis / Q000295:instrumentation* / Q000379:methods; D005260:Female; D006225:Hand / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000503:physiopathology; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods; D014159:Transducers / Q000639:trends