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On the choice of multiscale entropy algorithm for quantification of complexity in gait data

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Standard

On the choice of multiscale entropy algorithm for quantification of complexity in gait data. / Raffalt, Peter C.; Denton, William; Yentes, Jennifer M.

I: Computers in Biology and Medicine, Bind 103, 01.12.2018, s. 93-100.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Raffalt, PC, Denton, W & Yentes, JM 2018, 'On the choice of multiscale entropy algorithm for quantification of complexity in gait data', Computers in Biology and Medicine, bind 103, s. 93-100. https://doi.org/10.1016/j.compbiomed.2018.10.008

APA

Raffalt, P. C., Denton, W., & Yentes, J. M. (2018). On the choice of multiscale entropy algorithm for quantification of complexity in gait data. Computers in Biology and Medicine, 103, 93-100. https://doi.org/10.1016/j.compbiomed.2018.10.008

Vancouver

Raffalt PC, Denton W, Yentes JM. On the choice of multiscale entropy algorithm for quantification of complexity in gait data. Computers in Biology and Medicine. 2018 dec 1;103:93-100. https://doi.org/10.1016/j.compbiomed.2018.10.008

Author

Raffalt, Peter C. ; Denton, William ; Yentes, Jennifer M. / On the choice of multiscale entropy algorithm for quantification of complexity in gait data. I: Computers in Biology and Medicine. 2018 ; Bind 103. s. 93-100.

Bibtex

@article{3dfbb84a3c4440f0928ed319fc051228,
title = "On the choice of multiscale entropy algorithm for quantification of complexity in gait data",
abstract = "The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate. These two algorithms were applied to stride-to-stride time interval time series recorded from fourteen healthy subjects during one hour of overground and treadmill walking. In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time series between the two walking conditions. Thus, the present study recommends the use of either MSE or RCMSE for quantification of complexity in stride-to-stride time interval time series.",
keywords = "Stride time fluctuations, Overground, Treadmill, Walking, Nonlinear dynamics, Methodology",
author = "Raffalt, {Peter C.} and William Denton and Yentes, {Jennifer M.}",
year = "2018",
month = dec,
day = "1",
doi = "10.1016/j.compbiomed.2018.10.008",
language = "English",
volume = "103",
pages = "93--100",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - On the choice of multiscale entropy algorithm for quantification of complexity in gait data

AU - Raffalt, Peter C.

AU - Denton, William

AU - Yentes, Jennifer M.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate. These two algorithms were applied to stride-to-stride time interval time series recorded from fourteen healthy subjects during one hour of overground and treadmill walking. In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time series between the two walking conditions. Thus, the present study recommends the use of either MSE or RCMSE for quantification of complexity in stride-to-stride time interval time series.

AB - The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate. These two algorithms were applied to stride-to-stride time interval time series recorded from fourteen healthy subjects during one hour of overground and treadmill walking. In general, acceptable sensitivity and good parameter consistency were observed for both algorithms; however, they were not able to differentiate the complexity of the stride-to-stride time interval time series between the two walking conditions. Thus, the present study recommends the use of either MSE or RCMSE for quantification of complexity in stride-to-stride time interval time series.

KW - Stride time fluctuations

KW - Overground

KW - Treadmill

KW - Walking

KW - Nonlinear dynamics

KW - Methodology

U2 - 10.1016/j.compbiomed.2018.10.008

DO - 10.1016/j.compbiomed.2018.10.008

M3 - Journal article

C2 - 30343216

VL - 103

SP - 93

EP - 100

JO - Computers in Biology and Medicine

JF - Computers in Biology and Medicine

SN - 0010-4825

ER -

ID: 211808379