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22 December 2015 Ipsilateral coordination features for automatic classification of Parkinson's disease
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Proceedings Volume 9681, 11th International Symposium on Medical Information Processing and Analysis; 96810L (2015) https://doi.org/10.1117/12.2211469
Event: 11th International Symposium on Medical Information Processing and Analysis (SIPAIM 2015), 2015, Cuenca, Ecuador
Abstract
A reliable diagnosis of the Parkinson Disease lies on the objective evaluation of different motor sub-systems. Discovering specific motor patterns associated to the disease is fundamental for the development of unbiased assessments that facilitate the disease characterization, independently of the particular examiner. This paper proposes a new objective screening of patients with Parkinson, an approach that optimally combines ipsilateral global descriptors. These ipsilateral gait features are simple upper-lower limb relationships in frequency and relative phase spaces. These low level characteristics feed a simple SVM classifier with a polynomial kernel function. The strategy was assessed in a binary classification task, normal against Parkinson, under a leave-one-out scheme in a population of 16 Parkinson patients and 7 healthy control subjects. Results showed an accuracy of 94;6% using relative phase spaces and 82;1% with simple frequency relations.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fernanda Sarmiento, Angélica Atehortúa, Fabio Martínez, and Eduardo Romero "Ipsilateral coordination features for automatic classification of Parkinson's disease", Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96810L (22 December 2015); https://doi.org/10.1117/12.2211469
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