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- W2210458434 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." @default.
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- W2210458434 date "2015-12-22" @default.
- W2210458434 modified "2023-09-26" @default.
- W2210458434 title "Ipsilateral coordination features for automatic classification of Parkinson's disease" @default.
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- W2210458434 doi "https://doi.org/10.1117/12.2211469" @default.
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