On the use of approximate entropy and sample entropy with centre of pressure time-series
Abstract 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 lengt...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2018-12-01
|
| Series: | Journal of NeuroEngineering and Rehabilitation |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12984-018-0465-9 |
| _version_ | 1818837018450329600 |
|---|---|
| author | Luis Montesinos Rossana Castaldo Leandro Pecchia |
| author_facet | Luis Montesinos Rossana Castaldo Leandro Pecchia |
| author_sort | Luis Montesinos |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-19T03:15:50Z |
| format | Article |
| id | doaj.art-6eae7318718047f6a32fcf3d56cbca4e |
| institution | Directory Open Access Journal |
| issn | 1743-0003 |
| language | English |
| last_indexed | 2024-12-19T03:15:50Z |
| publishDate | 2018-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj.art-6eae7318718047f6a32fcf3d56cbca4e2022-12-21T20:37:53ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032018-12-0115111510.1186/s12984-018-0465-9On the use of approximate entropy and sample entropy with centre of pressure time-seriesLuis Montesinos0Rossana Castaldo1Leandro Pecchia2School of Engineering, University of WarwickSchool of Engineering, University of WarwickSchool of Engineering, University of WarwickAbstract 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.http://link.springer.com/article/10.1186/s12984-018-0465-9Approximate entropySample entropyHuman balancePostural controlPosturographyCentre of pressure |
| spellingShingle | Luis Montesinos Rossana Castaldo Leandro Pecchia On the use of approximate entropy and sample entropy with centre of pressure time-series Journal of NeuroEngineering and Rehabilitation Approximate entropy Sample entropy Human balance Postural control Posturography Centre of pressure |
| title | On the use of approximate entropy and sample entropy with centre of pressure time-series |
| title_full | On the use of approximate entropy and sample entropy with centre of pressure time-series |
| title_fullStr | On the use of approximate entropy and sample entropy with centre of pressure time-series |
| title_full_unstemmed | On the use of approximate entropy and sample entropy with centre of pressure time-series |
| title_short | On the use of approximate entropy and sample entropy with centre of pressure time-series |
| title_sort | on the use of approximate entropy and sample entropy with centre of pressure time series |
| topic | Approximate entropy Sample entropy Human balance Postural control Posturography Centre of pressure |
| url | http://link.springer.com/article/10.1186/s12984-018-0465-9 |
| work_keys_str_mv | AT luismontesinos ontheuseofapproximateentropyandsampleentropywithcentreofpressuretimeseries AT rossanacastaldo ontheuseofapproximateentropyandsampleentropywithcentreofpressuretimeseries AT leandropecchia ontheuseofapproximateentropyandsampleentropywithcentreofpressuretimeseries |