Entropy-Based Complexity Measures For Gait Data Of Patients With Parkinson'S Disease
| Autor: | Juergen Kurths, Ozgur Afsar, Ugur Tirnakli |
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| Rok vydání: | 2016 |
| Předmět: |
Risk predictor
Parkinson's disease Kullback–Leibler divergence Time Factors Entropy General Physics and Astronomy STRIDE 01 natural sciences 010305 fluids & plasmas 03 medical and health sciences 0302 clinical medicine Disease severity 0103 physical sciences Statistics medicine Entropy (information theory) Humans Gait Mathematical Physics Mathematics Applied Mathematics Case-control study Statistical and Nonlinear Physics Parkinson Disease medicine.disease Biomechanical Phenomena Gait analysis Case-Control Studies human activities 030217 neurology & neurosurgery |
| Popis: | Shannon, Kullback-Leibler, and Klimontovich's renormalized entropies are applied as three different complexity measures on gait data of patients with Parkinson's disease (PD) and healthy control group. We show that the renormalized entropy of variability of total reaction force of gait is a very efficient tool to compare patients with respect to disease severity. Moreover, it is a good risk predictor such that the sensitivity, i.e., the percentage of patients with PD who are correctly identified as having PD, increases from 25% to 67% while the Hoehn-Yahr stage increases from 2.5 to 3.0 (this stage goes from 0 to 5 as the disease severity increases). The renormalized entropy method for stride time variability of gait is found to correctly identify patients with a sensitivity of 80%, while the Shannon entropy and the Kullback-Leibler relative entropy can do this with a sensitivity of only 26.7% and 13.3%, respectively. (C) 2016 AIP Publishing LLC. |
| Databáze: | OpenAIRE |
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