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On the use of approximate entropy and sample entropy with centre of pressure time-series
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) an...
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Published in: | Journal of neuroengineering and rehabilitation 2018-12, Vol.15 (1), p.116-116, Article 116 |
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description | 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.
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 |
doi_str_mv | 10.1186/s12984-018-0465-9 |
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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.
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.
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.</description><identifier>ISSN: 1743-0003</identifier><identifier>EISSN: 1743-0003</identifier><identifier>DOI: 10.1186/s12984-018-0465-9</identifier><identifier>PMID: 30541587</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Adult ; Adults ; Aged ; Algorithms ; Approximate entropy ; Balance ; Centre of pressure ; Computer Simulation ; Entropy ; Entropy (Thermodynamics) ; Falls ; Female ; Human balance ; Humans ; Male ; Mathematical analysis ; Middle Aged ; Models, Biological ; Older people ; Parameter sensitivity ; Physiology ; Postural Balance - physiology ; Postural control ; Posture ; Posturography ; Pressure ; Sample entropy ; Senses ; Time series ; Time series analysis ; Variance analysis ; Young Adult ; Young adults</subject><ispartof>Journal of neuroengineering and rehabilitation, 2018-12, Vol.15 (1), p.116-116, Article 116</ispartof><rights>COPYRIGHT 2018 BioMed Central Ltd.</rights><rights>Copyright © 2018. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>The Author(s). 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c608t-e280ec5b47e8ec572b01a0e1539ec3a3b4b1c676b0c3cf3c0be5c3f775d4022c3</citedby><cites>FETCH-LOGICAL-c608t-e280ec5b47e8ec572b01a0e1539ec3a3b4b1c676b0c3cf3c0be5c3f775d4022c3</cites><orcidid>0000-0002-7900-5415</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6291990/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2158376854?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30541587$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Montesinos, Luis</creatorcontrib><creatorcontrib>Castaldo, Rossana</creatorcontrib><creatorcontrib>Pecchia, Leandro</creatorcontrib><title>On the use of approximate entropy and sample entropy with centre of pressure time-series</title><title>Journal of neuroengineering and rehabilitation</title><addtitle>J Neuroeng Rehabil</addtitle><description>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.
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.
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.
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.</description><subject>Adult</subject><subject>Adults</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Approximate entropy</subject><subject>Balance</subject><subject>Centre of pressure</subject><subject>Computer Simulation</subject><subject>Entropy</subject><subject>Entropy (Thermodynamics)</subject><subject>Falls</subject><subject>Female</subject><subject>Human balance</subject><subject>Humans</subject><subject>Male</subject><subject>Mathematical analysis</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Older people</subject><subject>Parameter sensitivity</subject><subject>Physiology</subject><subject>Postural Balance - physiology</subject><subject>Postural control</subject><subject>Posture</subject><subject>Posturography</subject><subject>Pressure</subject><subject>Sample entropy</subject><subject>Senses</subject><subject>Time series</subject><subject>Time series analysis</subject><subject>Variance analysis</subject><subject>Young Adult</subject><subject>Young adults</subject><issn>1743-0003</issn><issn>1743-0003</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptUk1v1DAQtRCIlsIP4IIiceGSMo4_c0GqKj4qVeoFJG6W40x2vUriYCdA_z3Obmm7CPkw4_F7bzyjR8hrCueUavk-0arWvASqS-BSlPUTckoVZyUAsKeP8hPyIqVdTjgI_pycsByo0OqUfL8Zi3mLxZKwCF1hpymG336wMxY4zjFMt4Ud2yLZYeofSr_8vC3cetuzpogpLTmf_YBlwugxvSTPOtsnfHUXz8i3Tx-_Xn4pr28-X11eXJdOgp5LrDSgEw1XqHNUVQPUAlLBanTMsoY31EklG3DMdcxBg8KxTinRcqgqx87I1UG3DXZnppj_Hm9NsN7sCyFujI2zdz0aiRYVo1pRDVx10rKqy5qtkK5xlmPW-nDQmpZmwHY_oO2PRI9fRr81m_DTyKqmdQ1Z4N2dQAw_FkyzGXxy2Pd2xLAkU1Ehag5AZYa-_Qe6C0sc86pWlGZKasEfUBubB_BjF3Jft4qaCyFzR6C1yqjz_6DyaXHwLozY-Vw_ItADwcWQUsTufkYKZrWWOVjLZGuZ1Vqmzpw3j5dzz_jrJfYHDfbJUQ</recordid><startdate>20181212</startdate><enddate>20181212</enddate><creator>Montesinos, Luis</creator><creator>Castaldo, Rossana</creator><creator>Pecchia, Leandro</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7QO</scope><scope>7RV</scope><scope>7TB</scope><scope>7TK</scope><scope>7TS</scope><scope>7X7</scope><scope>7XB</scope><scope>88C</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>L6V</scope><scope>LK8</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-7900-5415</orcidid></search><sort><creationdate>20181212</creationdate><title>On the use of approximate entropy and sample entropy with centre of pressure time-series</title><author>Montesinos, Luis ; Castaldo, Rossana ; Pecchia, Leandro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c608t-e280ec5b47e8ec572b01a0e1539ec3a3b4b1c676b0c3cf3c0be5c3f775d4022c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adult</topic><topic>Adults</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Approximate entropy</topic><topic>Balance</topic><topic>Centre of pressure</topic><topic>Computer Simulation</topic><topic>Entropy</topic><topic>Entropy (Thermodynamics)</topic><topic>Falls</topic><topic>Female</topic><topic>Human balance</topic><topic>Humans</topic><topic>Male</topic><topic>Mathematical analysis</topic><topic>Middle Aged</topic><topic>Models, Biological</topic><topic>Older people</topic><topic>Parameter sensitivity</topic><topic>Physiology</topic><topic>Postural Balance - physiology</topic><topic>Postural control</topic><topic>Posture</topic><topic>Posturography</topic><topic>Pressure</topic><topic>Sample entropy</topic><topic>Senses</topic><topic>Time series</topic><topic>Time series analysis</topic><topic>Variance analysis</topic><topic>Young Adult</topic><topic>Young adults</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Montesinos, Luis</creatorcontrib><creatorcontrib>Castaldo, Rossana</creatorcontrib><creatorcontrib>Pecchia, Leandro</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Physical Education Index</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Engineering Collection</collection><collection>Biological Sciences</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of neuroengineering and rehabilitation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Montesinos, Luis</au><au>Castaldo, Rossana</au><au>Pecchia, Leandro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On the use of approximate entropy and sample entropy with centre of pressure time-series</atitle><jtitle>Journal of neuroengineering and rehabilitation</jtitle><addtitle>J Neuroeng Rehabil</addtitle><date>2018-12-12</date><risdate>2018</risdate><volume>15</volume><issue>1</issue><spage>116</spage><epage>116</epage><pages>116-116</pages><artnum>116</artnum><issn>1743-0003</issn><eissn>1743-0003</eissn><abstract>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.
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.
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.
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.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>30541587</pmid><doi>10.1186/s12984-018-0465-9</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0002-7900-5415</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adult Adults Aged Algorithms Approximate entropy Balance Centre of pressure Computer Simulation Entropy Entropy (Thermodynamics) Falls Female Human balance Humans Male Mathematical analysis Middle Aged Models, Biological Older people Parameter sensitivity Physiology Postural Balance - physiology Postural control Posture Posturography Pressure Sample entropy Senses Time series Time series analysis Variance analysis Young Adult Young adults |
title | On the use of approximate entropy and sample entropy with centre of pressure time-series |
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