Motor disorders in Parkinson’s Disease (PD) show high inter-individual variability which challenges the current observational-based strategies in the clinical setting to determine the actual disease evolution and monitoring the therapy response. In spite the recent development of the motion capture technology, it is still hardly transferable to the routine exam and the non-linear disease patterns are poorly explored. Because gait pattern could be approached as deterministic chaotic system, this work aimed to non-linearly represent lower limb kinematic standing out the differences among PD stages. For doing so, a widely used deep learning framework was implemented for obtaining the body landmarks and their temporal series and thereafter, construing the phase space based on the first order derivatives. Largest Lyapunov exponent, correlation dimension and approximate entropy were then computed resulting in statistically significant differences (Wilcoxon rank test, p < 0.05), particularly between healthy controls and stages 3, the most advanced stage, and comparing stage 1 face to stage 3. These finding providing insights how the complex patterns may be related with the disease progression in PD and easily implemented using affordable video devices.
Motor disorders in Parkinson’s Disease (PD) show high inter-individual variability which challenges the current observational-based strategies in the clinical setting to determine the actual disease evolution and monitoring the therapy response. In spite the recent development of the motion capture technology, it is still hardly transferable to the routine exam and the non-linear disease patterns are poorly explored. Because gait pattern could be approached as deterministic chaotic system, this work aimed to non-linearly represent lower limb kinematic standing out the differences among PD stages. For doing so, a widely used deep learning framework was implemented for obtaining the body landmarks and their temporal series and thereafter, construing the phase space based on the first order derivatives. Largest Lyapunov exponent, correlation dimension and approximate entropy were then computed resulting in statistically significant differences (Wilcoxon rank test, p < 0.05), particularly between healthy controls and stages 3, the most advanced stage, and comparing stage 1 face to stage 3. These finding providing insights how the complex patterns may be related with the disease progression in PD and easily implemented using affordable video devices.
Observation of Gait patterns is the available evaluation in clinical routine of the motor manifestations in Parkinson’s Disease (PD). Lately, different investigations have attempted to quantitatively analyze gait patterns by linear methods facing several limitations since the non-stationary nature of the gait patterns. This study presents a non-linear characterization of the Parkinson's disease gait by a deterministic chaotic analysis which represents the temporal gait dynamics with a minimum set of parameters. Specifically, delay and embedding dimension parameters were obtained for reconstructing the phase space and its characteristic coefficients, namely Lyapunov, correlation dimension, and approximate entropy. Statistical differences (p < 0.05, Mann-Whitney test) were found for the Lyapunov exponent and the approximated entropy when describing two gait patterns, i.e., control and PD groups.
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