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• W2897747224 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." @default.
• W2897747224 created "2018-10-26" @default.
• W2897747224 creator A5019350881 @default.
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• W2897747224 date "2018-12-01" @default.
• W2897747224 modified "2023-09-26" @default.
• W2897747224 title "On the choice of multiscale entropy algorithm for quantification of complexity in gait data" @default.
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• W2897747224 doi "https://doi.org/10.1016/j.compbiomed.2018.10.008" @default.
• W2897747224 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6957257" @default.
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