On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data

McCamley, John; Denton, William; Arnold, Andrew; Raffalt, Peter C.; Yentes, Jennifer M.

doi:10.3390/e20100764

Cited by 44 publications

(37 citation statements)

References 38 publications

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“…The time-delay of the signal was calculated using mutual information method for all the continuous walks [61], and the average time-delay of all the continuous walks was used to calculate the SamplEn for each volunteer. We used embedding dimension m = 3, and tolerance r = 0.2 times the standard deviation of the signal, which are commonly used to compute sample entropy of gait signal [41,[57][58][59][60].…”

Section: Gait Irregularitymentioning

confidence: 99%

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

et al. 2020

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Background: Frailty is a highly recognized geriatric syndrome resulting in decline in reserve across multiple physiological systems. Impaired physical function is one of the major indicators of frailty. The goal of this study was to evaluate an algorithm that discriminates between frailty groups (non-frail and pre-frail/frail) based on gait performance parameters derived from unsupervised daily physical activity (DPA). Methods: DPA was acquired for 48 h from older adults (≥65 years) using a tri-axial accelerometer motion-sensor. Continuous bouts of walking for 20s, 30s, 40s, 50s and 60s without pauses were identified from acceleration data. These were then used to extract qualitative measures (gait variability, gait asymmetry, and gait irregularity) and quantitative measures (total continuous walking duration and maximum number of continuous steps) to characterize gait performance. Association between frailty and gait performance parameters was assessed using multinomial logistic models with frailty as the dependent variable, and gait performance parameters along with demographic parameters as independent variables. Results: One hundred twenty-six older adults (44 non-frail, 60 pre-frail, and 22 frail, based on the Fried index) were recruited. Step-and stride-times, frequency domain gait variability, and continuous walking quantitative measures were significantly different between non-frail and pre-frail/frail groups (p < 0.05). Among the five different durations (20s, 30s, 40s, 50s and 60s), gait performance parameters extracted from 60s continuous walks provided the best frailty assessment results. Using the 60s gait performance parameters in the logistic model, pre-frail/frail group (vs. non-frail) was identified with 76.8% sensitivity and 80% specificity. Discussion: Everyday walking characteristics were found to be associated with frailty. Along with quantitative measures of physical activity, qualitative measures are critical elements representing the early stages of frailty. In-home gait assessment offers an opportunity to screen for and monitor frailty. Trial registration: The clinical trial was retrospectively registered on June 18th, 2013 with ClinicalTrials.gov, identifier NCT01880229.

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Section: Gait Irregularitymentioning

confidence: 99%

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

et al. 2020

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“…Another method that has gained attention and validation over recent years is using sample entropy (SaEn) of COP data [ 22 ]. SaEn can be described as a measure of regularity adapted to strings of data in a time series [ 16 , 23 ]. Research suggests that compromised postural control systems tend to produce more regular (low-SaEn-value) movements compared to healthy postural control systems, which typically show less regularity (high SaEn-value).…”

Section: Introductionmentioning

confidence: 99%

Changed Temporal Structure of Neuromuscular Control, Rather Than Changed Intersegment Coordination, Explains Altered Stabilographic Regularity after a Moderate Perturbation of the Postural Control System

Wachholz

Kockum

Haid

et al. 2019

Entropy

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Sample entropy (SaEn) applied on center-of-pressure (COP) data provides a measure for the regularity of human postural control. Two mechanisms could contribute to altered COP regularity: first, an altered temporal structure (temporal regularity) of postural movements (H1); or second, altered coordination between segment movements (coordinative complexity; H2). The current study used rapid, voluntary head-shaking to perturb the postural control system, thus producing changes in COP regularity, to then assess the two hypotheses. Sixteen healthy participants (age 26.5 ± 3.5; seven females), whose postural movements were tracked via 39 reflective markers, performed trials in which they first stood quietly on a force plate for 30 s, then shook their head for 10 s, finally stood quietly for another 90 s. A principal component analysis (PCA) performed on the kinematic data extracted the main postural movement components. Temporal regularity was determined by calculating SaEn on the time series of these movement components. Coordinative complexity was determined by assessing the relative explained variance of the first five components. H1 was supported, but H2 was not. These results suggest that moderate perturbations of the postural control system produce altered temporal structures of the main postural movement components, but do not necessarily change the coordinative structure of intersegment movements.

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“…Sample entropy can be used to describe nonlinear signals with a high complexity and a large computational requirement [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Cai

Peng

et al. 2019

Entropy

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Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

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On the Calculation of Sample Entropy Using Continuous and Discrete Human Gait Data

Cited by 44 publications

References 38 publications

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

Sensor-based characterization of daily walking: a new paradigm in pre-frailty/frailty assessment

Changed Temporal Structure of Neuromuscular Control, Rather Than Changed Intersegment Coordination, Explains Altered Stabilographic Regularity after a Moderate Perturbation of the Postural Control System

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

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