Nonlinear and Linear Measures in the Differentiation of Postural Control in Patients after Total Hip or Knee Replacement and Healthy Controls

Hadamus, Anna; Błażkiewicz, Michalina; Kowalska, Aleksandra J.; Wydra, Kamil T.; Grabowicz, Marta; Łukowicz, Małgorzata; Białoszewski, Dariusz; Marczyński, Wojciech

doi:10.3390/diagnostics12071595

Cited by 12 publications

(14 citation statements)

References 40 publications

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“…With an understanding of how ApEn and velocity of COP behave for varying levels of stability in neurotypical subjects, these indices may be useful to screen for conditions such as concussion in a more objective manner. Linear and nonlinear measures have been previously used in studies to determine changes to stability based on different neurological and physical impairments [46,47]. Although these studies compared a baseline control group to a group with impaired postural control as opposed to comparing a baseline condition of an individual to adjusted conditions of the same individual and utilized a different quiet standing methodology than this study, the clinical application of these measures on COP to distinguish postural control encourages the motivation for this work.…”

Section: Discussionmentioning

confidence: 99%

Approximate Entropy and Velocity of Center of Pressure to Determine Postural Stability: A Pilot Study

2023

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The body’s postural control system is responsible for responding to perturbations of balance and keeping the body upright. During quiet standing, the center of pressure oscillates about the center of mass, counteracting imbalances. These oscillations can be analyzed to determine the degree of stability, which could be helpful in quantifying the effects of brain injuries. In this research, the center of pressure was recorded for stances with feet together and feet tandem, with eyes opened and eyes closed, in neurotypical participants. These signals were analyzed using indices of approximate entropy and velocity to determine how sensitive the measures were in tracking changes to stability levels. One-way ANOVA test results showed increased approximate entropy in anterior/posterior and medial/lateral directions (p = 1.21 × 10−11, 3 × 10−14) and increased velocity in both directions (p = 2.87 × 10−6, 4.87 × 10−7) during conditions with decreased stability. Dunnett’s post hoc testing indicated that approximate entropy was significantly greater in all the less stable feet tandem trials in comparison to the most stable eyes open, feet together condition with p < 0.001 in nearly every participant and that velocity was significantly greater in the least stable eyes closed, feet tandem trials in comparison to the most stable condition with p < 0.01 in nearly every participant.

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Section: Discussionmentioning

confidence: 99%

Approximate Entropy and Velocity of Center of Pressure to Determine Postural Stability: A Pilot Study

2023

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“…considered two features that capture signal complexity: Approximate Entropy (ApEn) [28], and Lyapunov Exponent (LyExp) [22], [29].…”

Section: A Data Processing For Postural Sway Parametersmentioning

confidence: 99%

“…To address these challenges, studies have shown that postural sway can be assessed using data from just a sacral accelerometer [16], [20], [21]. Sacral sensor-derived postural sway measures have been used to classify fall risk in PwMS [20], distinguish between disease states [16], [19], [22] and to augment current assessment techniques [23], thereby achieving similar clinical utility to the force platform.…”

Section: Introductionmentioning

confidence: 99%

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis

Meyer

Cohen

Donahue

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five of 15 postural sway features derived from the chest and sacrum were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a oneminute period with a sliding window to create individualized distributions of each postural sway feature (ID method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the ID method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the ID method over single-observation analyses.

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“…These features included thirteen features from Mancini et al [16]: Jerk, Distance (Dist), Root-Mean-Square (RMS), Path, Range, Mean Velocity (MV), Mean Frequency (MF), Area, Power (Pwr), median power frequency (F50), 95% power frequency (F95), Centroidal Frequency (CF), and Frequency Dispersion (FD). We also considered two features that capture signal complexity: Approximate Entropy (ApEn) [28], and Lyapunov Exponent (LyExp) [22], [29]. Process overview of subject specific distribution (SSD) and a 30-second single observation (SO) methods.…”

Section: A Data Processing For Postural Sway Parametersmentioning

confidence: 99%

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons with Multiple Sclerosis

Meyer¹,

Cohen²,

Donahue³

et al. 2023

Preprint

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<p>Typical assessments of balance impairment are subjective or require data from cumbersome and expensive force platforms. Researchers have utilized lower back (sacrum) accelerometers to enable more accessible, objective measurement of postural sway for use in balance assessment. However, new sensor patches are broadly being deployed on the chest for cardiac monitoring, opening a need to determine if measurements from these devices can similarly inform balance assessment. Our aim in this work is to validate postural sway measurements from a chest accelerometer. To establish concurrent validity, we considered data from 16 persons with multiple sclerosis (PwMS) asked to stand on a force platform while also wearing sensor patches on the sacrum and chest. We found five (five) of 15 postural sway features derived from chest (sacrum) were significantly correlated with force platform-derived features, which is in line with prior sacrum-derived findings. Clinical significance was established using a sample of 39 PwMS who performed eyes-open, eyes-closed, and tandem standing tasks. This cohort was stratified by fall status and completed several patient-reported measures (PRM) of balance and mobility impairment. We also compared sway features derived from a single 30-second period to those derived from a one-minute period with a sliding window to create subject-specific distributions of each postural sway feature (SSD method). We find traditional computation of sway features from the chest is sensitive to changes in PRMs and task differences. Distribution characteristics from the SSD method establish additional relationships with PRMs, detect differences in more tasks, and distinguish between fall status groups. Overall, the chest was found to be a valid location to monitor postural sway and we recommend utilizing the SSD method over single-observation analyses. </p>

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Nonlinear and Linear Measures in the Differentiation of Postural Control in Patients after Total Hip or Knee Replacement and Healthy Controls

Cited by 12 publications

References 40 publications

Approximate Entropy and Velocity of Center of Pressure to Determine Postural Stability: A Pilot Study

Approximate Entropy and Velocity of Center of Pressure to Determine Postural Stability: A Pilot Study

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons With Multiple Sclerosis

Chest-Based Wearables and Individualized Distributions for Assessing Postural Sway in Persons with Multiple Sclerosis

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