Year: 2012
Type: conference-paper
Study design: Observational Study
Abstract: Using machine learning based models in clinical applications has become current practice and can prove useful to provide information at the subject's level, such as predicting an (early) diagnosis or monitoring the evolution of a disease.However, the performance of these models depends on the choice of a biomarker to detect the presence or absence of a disease.Choosing a biomarker is not straightforward, especially in the case of Parkinson's disease when compared to healthy subjects.In the present work, we investigated the mental imagery of gait as a biomarker of Parkinson's disease and showed that the signal in the mesencephalic locomotor region during the mental imagery of gait at a comfortable pace can discriminate significantly between idiopathic Parkinson's disease patients and healthy subjects.Although there is room for improvement, the results of this preliminary study are promising.
Source: ORBi (University of Liège)
Language: English
Subfield: Clinical Neurology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2279367209