Dynamical analysis of balance in vestibular schwannoma patients.

Gait Posture

Department of Oto-Rhino-Laryngology, Head and Neck Surgery, University Hospital of Nancy, 29 avenue du Maréchal de Lattre de Tassigny, F-54000 Nancy, France; Laboratory "Développement, Adaptation et Handicap" (DevAH - EA 3450), Faculty of Sports Science and Faculty of Medicine, University of Lorraine, 9 avenue de la Forêt de Haye, CS 50184, F-54505 Vandœuvre-lès-Nancy, France.

Published: May 2017

The analysis of the complexity of postural fluctuations is a recent method for assessing postural control. Complexity relates to the irregularity of the center of pressure time series and characterizes the ability of postural control to meet a changing environment. In our study, we used the sample entropy (SampEn) parameter to evaluate the complexity of postural sway velocity time series in patients with vestibular schwannoma (n=19) compared to healthy controls (n=20), using the sensory organization test. Patients performed postural assessments three days before surgical ablation of the tumor, then three times after surgery, at eight, thirty, and ninety days. The control group underwent posturographic tests only once. Our results demonstrated that SampEn values distinguished both groups before surgery only in postural tasks where vestibular afferences significantly contribute to maintaining balance. We also found an immediate decrease of complexity after the surgical resection of the tumor. Our results are in line with the theory of complexity loss of physiological systems stating that reducing the number of their structural components or altering their coupling leads to a decrease in complexity. Finally, our findings showed that progressive restoration of complexity over time was such that no difference was found between the two groups ninety days after surgery, due to the implementation of central adaptive mechanisms and the substitution by other sensory afferences. Thus, the SampEn parameter can highlight the postural effects of vestibular pathology, and complexity analysis appears to be a valuable tool for investigating the temporal structure of CoP time series.

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Source
http://dx.doi.org/10.1016/j.gaitpost.2017.03.015DOI Listing

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