Dynamical analysis of balance in vestibular schwannoma patients

Hoffmann, Charles; Seigle, Benoît; Frère, Julien; Parietti‐Winkler, Cécile

doi:10.1016/j.gaitpost.2017.03.015

Cited by 16 publications

(10 citation statements)

References 28 publications

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“…Note that the comparison of values is made possible, although the assessment was done in 2 separate studies, because we processed the signal in the same manner in the 2 studies (down-sampling by 2, no smoothing applied) [26] and used the same parameters to calculate SampEn (r, m) [27]. Our findings are in agreement with previous studies on environmental constraints [7,8]: regularity increases (ie, SampEn decreases) when participants are standing on a compliant surface compared with a stable floor and when visual complexity increases (ie, dark vs light, larger sway of stars compared with minimal sway). In fact, the effect of the support surface on regularity was so dominant that any between-group differences vanished when participants were standing on the stability trainer.…”

Section: Discussionsupporting

confidence: 86%

“…This supports the notion that visual dependence increases with age and vestibular dysfunction, but adults with vestibular dysfunction are more challenged with the further decrease of somatosensory input because the vestibular system is important for upright control, especially as the support surface becomes more challenging [32]. Challenges in sensory integration with vestibular dysfunction have been reported in the literature, where differences between healthy adults and adults with vestibular dysfunction have been reported primarily on the more challenging conditions of the SOT [3,8]. One aspect that is unique in our paradigm is its mild nature.…”

Section: Discussionsupporting

confidence: 77%

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Control Mechanisms of Static and Dynamic Balance in Adults With and Without Vestibular Dysfunction in Oculus Virtual Environments

Lubetzky

Hujsak

Kelly

et al. 2018

PM&R

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Background Deficits in sensory integration and fear of falling in complex environments contribute to decreased participation of adults with vestibular disorders. With recent advances in virtual reality technology, head‐mounted displays are affordable and allow manipulation of the environment to test postural responses to visual changes. Objectives To develop an assessment of static and dynamic balance with the Oculus Rift and (1) to assess test‐retest reliability of each scene in adults with and without vestibular hypofunction; (2) to describe changes in directional path and sample entropy in response to changes in visuals and surface and compare between groups; and (3) to evaluate the relation between balance performance and self‐reported disability and balance confidence. Design Test‐retest, blocked‐randomized experimental design. Setting Research laboratory. Participants Twenty‐five adults with vestibular hypofunction and 16 age‐ and sex‐matched adults. Methods Participants stood on the floor or stability trainers while wearing the Oculus Rift. For 3 moving “stars” scenes, they stood naturally. For a “park” scene, they were asked to avoid a virtual ball. The protocol was repeated 1‐4 weeks later. Outcome Anteroposterior and mediolateral center‐of‐pressure directional path and sample entropy were derived from a force plate. Results We observed good to excellent reliability in the 2 groups, with most intraclass correlations above 0.8 and only 2 at approximately 0.4. The vestibular group had higher directional path for the stars scenes and lower directional path for the park scene compared with controls, with large variability in the 2 groups. Sample entropy decreased with more challenging environments. In the vestibular group, less balance confidence strongly correlated with more sway for the stars scenes and less sway for the park scene. Conclusion Virtual reality paradigms can shed light on the control mechanism of static and dynamic postural control. Clinical utility and implementation of our portable Oculus Rift assessment should be further studied. Level of Evidence II

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Section: Discussionsupporting

confidence: 86%

Section: Discussionsupporting

confidence: 77%

Control Mechanisms of Static and Dynamic Balance in Adults With and Without Vestibular Dysfunction in Oculus Virtual Environments

Lubetzky

Hujsak

Kelly

et al. 2018

PM&R

View full text Add to dashboard Cite

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“…Since its introduction in 2000, SampEn has been used to study postural control given various experimental conditions [ 4 , 5 ], to assess effects of disease or age [ 6 , 7 ], as an indication of balance [ 8 ] or to compare performance before and after interventions [ 4 , 9 ]. Nevertheless, large variability in the handling of the signal and the choice of parameters makes the comparison between studies unfeasible.…”

Section: Introductionmentioning

confidence: 99%

“…Addressing the choice of parameters ( r , m ), Yentes et al found m = 2 to be optimal for gait and theoretical data [ 10 ]. Postural control studies typically utilized either m = 2 (e.g., [ 5 , 9 , 11 ]) or m = 3 (e.g., [ 4 , 6 , 8 ]). Yentes et al also suggested that, for gait data, the radius of similarity should be examined per the experimental conditions.…”

Section: Introductionmentioning

confidence: 99%

“…When it comes to SampEn, however, it has been suggested that a traditional filter may smooth out the unique features of the time series [ 19 , 20 ]. The literature is diverse here as well with several studies applying a filter [ 4 – 6 ], others applying down-sampling techniques [ 7 , 11 , 21 ] and others analyzing the original data set [ 8 , 9 ]. These differences in data processing techniques have been shown to impact the value of SampEn, especially following down-sampling to 25 Hz or when SampEn was derived from differenced data [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

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On the effects of signal processing on sample entropy for postural control

2018

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Sample entropy, a measure of time series regularity, has become increasingly popular in postural control research. We are developing a virtual reality assessment of sensory integration for postural control in people with vestibular dysfunction and wished to apply sample entropy as an outcome measure. However, despite the common use of sample entropy to quantify postural sway, we found lack of consistency in the literature regarding center-of-pressure signal manipulations prior to the computation of sample entropy. We therefore wished to investigate the effect of parameters choice and signal processing on participants’ sample entropy outcome. For that purpose, we compared center-of-pressure sample entropy data between patients with vestibular dysfunction and age-matched controls. Within our assessment, participants observed virtual reality scenes, while standing on floor or a compliant surface. We then analyzed the effect of: modification of the radius of similarity (r) and the embedding dimension (m); down-sampling or filtering and differencing or detrending. When analyzing the raw center-of-pressure data, we found a significant main effect of surface in medio-lateral and anterior-posterior directions across r’s and m’s. We also found a significant interaction group × surface in the medio-lateral direction when r was 0.05 or 0.1 with a monotonic increase in p value with increasing r in both m’s. These effects were maintained with down-sampling by 2, 3, and 4 and with detrending but not with filtering and differencing. Based on these findings, we suggest that for sample entropy to be compared across postural control studies, there needs to be increased consistency, particularly of signal handling prior to the calculation of sample entropy. Procedures such as filtering, differencing or detrending affect sample entropy values and could artificially alter the time series pattern. Therefore, if such procedures are performed they should be well justified.

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