Evaluation of the temporal structure of postural sway fluctuations based on a comprehensive set of analysis tools

Kirchner, Marietta; Schubert, P.; Schmidtbleicher, Dietmar; Haas, Christian T.

doi:10.1016/j.physa.2012.05.034

Cited by 55 publications

(55 citation statements)

References 54 publications

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“…Estimates for the minimum number of SampEn points are sometimes based on theoretical calculations for ApEn which suggest that 10 m points should be sufficient, although 20 m -30 m points would be preferable for an accurate estimate [27,37]. However ApEn estimates based on simulated random time-series show increasing effects of self-matching bias with a smaller number of points [14].…”

Section: Determining Required Data Lengthmentioning

confidence: 99%

“…However, a larger r will result in a conditional property (A/B) of 1 and thus a SampEn of zero for nearly all stationary time series, thereby limiting one's ability to discriminate between various time series. On the other hand, r must be large enough to avoid the influence of noise and to simultaneously increase probability of matches to ensure that confidence in SampEn is adequate [27].…”

Section: Sequence Length (M) and Point Matching Tolerance (R)mentioning

confidence: 99%

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Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Gow

Peng

Wayne

et al. 2015

Entropy

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Multiscale entropy (MSE) is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP) dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE for characterizing COP displacement dynamics. We identified five key methodological procedures that varied significantly between studies: (1) data length; (2) frequencies of the COP dynamics analyzed; (3) sampling rate; (4) point matching tolerance and sequence length; and (5) filtering of displacement changes from drifts, fidgets, and shifts. We discuss strengths and limitations of the various approaches employed and supply flowcharts to assist in the decision making process regarding each of these procedures. Our guidelines are intended to more broadly inform the design and analysis of future studies employing MSE for continuous time series, such as COP.

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Section: Determining Required Data Lengthmentioning

confidence: 99%

Section: Sequence Length (M) and Point Matching Tolerance (R)mentioning

confidence: 99%

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Gow

Peng

Wayne

et al. 2015

Entropy

View full text Add to dashboard Cite

show abstract

“…Sixteen healthy students (9 males and 7 females, age: 26.1 ± 6.7 years; height: 173.45 ± 11.14 cm; weight: 72.36 ± 13.04 kg) without musculoskeletal or neurological dysfunctions participated voluntarily in this study [24]. The subjects were instructed to stand with both feet parallel and upright as quiet as possible in hip width stance with arms relaxed at both sides while staring at a point on the wall in front and concentrating on the postural control process, which induces an internal focus of attention [8].…”

Section: Methodsmentioning

confidence: 99%

“…Next, time series were downsampled to 100 Hz (for calculation of entropy values to 20 Hz) and detrended by the mean of the time series. This procedure is appropriate for COP data as 95% of the informational content is within a range of the first Hertz [24,28]. Impact effects were eliminated by cutting the first 5 s from the time series.…”

Section: Methodsmentioning

confidence: 99%

“…The question whether different sampling times necessitate a different set of COP parameters is still left open. The present study investigates the influence of three different sampling durations (30 s, 60 s and 300 s) on the postural performance of quiet standing in healthy subjects with respect to the most common linear, but also more sophisticated nonlinear-parameters of posturography [24]. For this purpose, and with respect to earlier studies [21,23] a factor analysis approach is conducted to expose the sensitivity of different parameter groups to different measurement times.…”

Section: Introductionmentioning

confidence: 99%

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About the structure of posturography: Sampling duration, parametrization, focus of attention (part I)

Schubert¹,

Kirchner²,

Schmidtbleicher³

et al. 2012

JBiSE

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This study investigates the choice of posturographic parameter sets with respect to the influence of different sampling durations (30 s, 60 s, 300 s). Center of pressure (COP) data are derived from 16 healthy subjects standing quietly on a force plate. They were advised to focus on the postural control process (i.e. internal focus of attention). 33 common linear and 10 nonlinear parameters are calculated and grouped into five classes. Component structure in each group is obtained via exploratory factor analysis. We demonstrate that COP evaluation-irrespective of sampling duration-necessitates a set of diverse parameters to explain more variance of the data. Further more, parameter sets are uniformly invariant towards sampling durations and display a consistent factor loading pattern. These findings pose a structure for COP parametrization. Hence, specific recommendations are preserved in order to avoid redundancy or misleading basis for inter-study comparisons. The choice of 11 parameters from the groups is recommended as a framework for future research in posturography.

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Atypical Head Movement during Face‐to‐Face Interaction in Children with Autism Spectrum Disorder

et al. 2021

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The present study implemented an objective head pose tracking technique—OpenFace 2.0 to quantify the three dimensional head movement. Children with autism spectrum disorder (ASD) and typical development (TD) were engaged in a structured conversation with an interlocutress while wearing an eye tracker. We computed the head movement stereotypy with multiscale entropy analysis. In addition, the head rotation range (RR) and the amount of rotation per minute (ARPM) were calculated to quantify the extent of head movement. Results demonstrated that the ASD group had significantly higher level of movement stereotypy, RR and ARPM in all the three directions of head movement. Further analyses revealed that the extent of head movement could be significantly explained by movement stereotypy, but not by the amount of visual fixation to the interlocutress. These results demonstrated the atypical head movement dynamics in children with ASD during live interaction. It is proposed that head movement might potentially provide novel objective biomarkers of ASD. Lay Summary Our study used an objective tool to quantify head movement in children with autism. Results showed that children with autism had more stereotyped and greater head movement. We suggest that head movement tracking technique be widely used in autism research.

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Evaluation of the temporal structure of postural sway fluctuations based on a comprehensive set of analysis tools

Cited by 55 publications

References 54 publications

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

About the structure of posturography: Sampling duration, parametrization, focus of attention (part I)

Atypical Head Movement during Face‐to‐Face Interaction in Children with Autism Spectrum Disorder

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