Asset Details
Do you wish to reserve the book?
On the use of approximate entropy and sample entropy with centre of pressure time-series
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
On the use of approximate entropy and sample entropy with centre of pressure time-series
Do you wish to request the book?
On the use of approximate entropy and sample entropy with centre of pressure time-series
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
On the use of approximate entropy and sample entropy with centre of pressure time-series
2018
Overview
Background
Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters:
m
(subseries length),
r
(tolerance) and
N
(data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters
m
,
r
and
N
on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups.
Methods
A public dataset of COP time-series was used. ApEn and SampEn were calculated for
m
= {2, 3, 4, 5},
r
= {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and
N
= {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60,
n
= 85), and older adults (age ≥ 60) with (
n
= 18) and without (
n
= 56) falls in the last year. The effects of changing parameters
m
,
r
and
N
on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors:
m
,
r
and
N
; between-subject factor: group). Specific combinations of
m
,
r
and
N
producing significant differences between groups were identified using the Tukey’s honest significant difference procedure.
Results
A significant three-way interaction between
m
,
r
and
N
confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (
N
= 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of
m
and
r
, highlighting the importance of an adequate selection of input parameters.
Conclusions
Future studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.
Publisher
BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
Subject
/ Adults
/ Aged
/ Balance
/ Biomedical and Life Sciences
/ Biomedical Engineering and Bioengineering
/ Entropy
/ Falls
/ Female
/ Humans
/ Male
/ Postural Balance - physiology
/ Posture
/ Pressure
/ Senses
