On the choice of multiscale entropy algorithm for quantification of complexity in gait data
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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 tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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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