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Detection of Levodopa Induced Dyskinesia in Parkinson's Disease Patients Based on Activity Classification

Abstract : In this paper, we present an activity classification-based algorithm for the automatic detection of Levodopa Induced Dyskinesia in Parkinson's Disease (PD) patients. Two PD patients experiencing motor fluctuations related to chronic Levodopa therapy performed a protocol of simple daily life activities on at least two different occasions. A Random Forest classifier was able to classify the performed activities by the patients with an overall accuracy of 86%. Based on the detected activity, a K Nearest Neighbor classifier detected the presence of dyskinesia with accuracy ranging from 75% to 88%
Keywords : Sensors
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Conference papers
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https://hal-univ-rennes1.archives-ouvertes.fr/hal-01304749
Contributor : Laurent Jonchère <>
Submitted on : Wednesday, April 20, 2016 - 11:53:41 AM
Last modification on : Wednesday, May 16, 2018 - 11:23:41 AM

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  • HAL Id : hal-01304749, version 1

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Nahed Jalloul, Fabienne Porée, Geoffrey Viardot, Philippe l'Hostis, Guy Carrault. Detection of Levodopa Induced Dyskinesia in Parkinson's Disease Patients Based on Activity Classification. 2015 37th annual international conference of the IEEE Engineering in Medicine and Biology Society, 2015, Milan, Italy. pp.5134--5137. ⟨hal-01304749⟩

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