Automated stage discrimination of Parkinson’s disease
Résumé
Treament plans for Parkinson’s disease are based on a disease stage scale, which is generally determined using a manual, observational procedure. Automated, sensor based discrimination saves labour and cost in clinical settings and may offer augmented stage determination accuracy. Previous automated devices were either cumbersome or costly and were not suitable for individuals who cannot walk without support.
Since 2017, a device has been available that successfully detects Parkinson’s disease and operates for people who cannot walk without support. In the present study, the suitability of this device for automated discrimination of Parkinson’s disease stages is tested. The device consists of a walking frame fitted with sensors to simultaneously support walking and monitor patient gait. Sixty-five Parkinson’s disease patients in HYstages 1 to 4 and twenty-four heathy controls were subjected to supported timed up and go (TUG) tests, while using the walking frame. The walking trajectory, velocity, acceleration and force were recorded by the device throughout the tests. These physical parameters were converted into symptomic spatio-temporal quanitities that are conventionaly used in Parksinon’s disease gait assessment.
An ANOVA Test extended by a confidence interval analysis indicated statistically significant seperability between HYstages for the following spatio-temporal quantities: TUG time (p<0.001), straight like walking time (p<0.001), turning time (p<0.001) and step count (p<0.001). A negative correlation was obtained for mean step velocity (p<0.001) and mean step length (p<0.001). Moreover, correlations were established between these, as well as additional spatio-temporal quanitities, and disease duration, levodopa dose, motor fluctuation, dyskinesia and the mobile part of the unified Parkinson’s disease rating scale.
We have proven that stage discrimination of Parkinson’s disease can be automated, even to patients who cannot support themselves. A similar method might be successfully applied to other gait disorders.
Domaines
Neurosciences
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