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Fractal analysis of surface electromyography signals: A novel power spectrum-based method
Abstract This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical se...
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Published in: | Journal of electromyography and kinesiology 2009-10, Vol.19 (5), p.840-850 |
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creator | Talebinejad, Mehran Chan, Adrian D.C Miri, Ali Dansereau, Richard M |
description | Abstract This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal’s interference pattern. These novel spectral indicators are capable of sensing the effects of motor units’ recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/ f α approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures. |
doi_str_mv | 10.1016/j.jelekin.2008.05.004 |
format | article |
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This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal’s interference pattern. These novel spectral indicators are capable of sensing the effects of motor units’ recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/ f α approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures.</description><identifier>ISSN: 1050-6411</identifier><identifier>EISSN: 1873-5711</identifier><identifier>DOI: 10.1016/j.jelekin.2008.05.004</identifier><identifier>PMID: 18617420</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adult ; Algorithms ; Diagnosis, Computer-Assisted - methods ; Electromyography - methods ; Electromyography signal ; Fatigue ; Force ; Fractal analysis ; Fractals ; Humans ; Joint angle ; Male ; Muscle Contraction - physiology ; Muscle, Skeletal - physiology ; Pattern Recognition, Automated - methods ; Physical Medicine and Rehabilitation ; Self-affinity</subject><ispartof>Journal of electromyography and kinesiology, 2009-10, Vol.19 (5), p.840-850</ispartof><rights>2008</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-10bb118bdadbcad44269d2f59a44d3e9dd4ef4b07244cff93540ab738216ee9b3</citedby><cites>FETCH-LOGICAL-c418t-10bb118bdadbcad44269d2f59a44d3e9dd4ef4b07244cff93540ab738216ee9b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18617420$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Talebinejad, Mehran</creatorcontrib><creatorcontrib>Chan, Adrian D.C</creatorcontrib><creatorcontrib>Miri, Ali</creatorcontrib><creatorcontrib>Dansereau, Richard M</creatorcontrib><title>Fractal analysis of surface electromyography signals: A novel power spectrum-based method</title><title>Journal of electromyography and kinesiology</title><addtitle>J Electromyogr Kinesiol</addtitle><description>Abstract This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal’s interference pattern. These novel spectral indicators are capable of sensing the effects of motor units’ recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/ f α approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Diagnosis, Computer-Assisted - methods</subject><subject>Electromyography - methods</subject><subject>Electromyography signal</subject><subject>Fatigue</subject><subject>Force</subject><subject>Fractal analysis</subject><subject>Fractals</subject><subject>Humans</subject><subject>Joint angle</subject><subject>Male</subject><subject>Muscle Contraction - physiology</subject><subject>Muscle, Skeletal - physiology</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Physical Medicine and Rehabilitation</subject><subject>Self-affinity</subject><issn>1050-6411</issn><issn>1873-5711</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><recordid>eNqFkc1u1TAQRi0EoqXwCCCv2CXMJM4fC1BVtVCpUheFBSvLsSet0yQOdlKUt8fRvRISm648izPfJ59h7D1CioDlpz7taaBHO6UZQJ1CkQKIF-wU6ypPigrxZZyhgKQUiCfsTQg9AFZQw2t2gnWJlcjglP268kovauBqUsMWbOCu42H1ndLEY4FevBs3d-_V_LDxYO8jFj7zcz65Jxr47P6Q52HeuXVMWhXI8JGWB2feslddZOnd8T1jP68uf1x8T25uv11fnN8kWmC9JAhti1i3RplWKyNEVjYm64pGCWFyaowR1IkWqkwI3XVNXghQbZXXGZZETZufsY-H3Nm73yuFRY42aBoGNZFbgyyrEstSNBEsDqD2LgRPnZy9HZXfJILcncpeHp3K3amEQkance_DsWBtRzL_to4SI_D1AFD85pMlL4O2NGky1kcx0jj7bMWX_xL0YCer1fBIG4XerX73LlGGTIK82w-73zVeM6ZUIv8LdG-hFQ</recordid><startdate>20091001</startdate><enddate>20091001</enddate><creator>Talebinejad, Mehran</creator><creator>Chan, Adrian D.C</creator><creator>Miri, Ali</creator><creator>Dansereau, Richard M</creator><general>Elsevier Ltd</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>7X8</scope></search><sort><creationdate>20091001</creationdate><title>Fractal analysis of surface electromyography signals: A novel power spectrum-based method</title><author>Talebinejad, Mehran ; Chan, Adrian D.C ; Miri, Ali ; Dansereau, Richard M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-10bb118bdadbcad44269d2f59a44d3e9dd4ef4b07244cff93540ab738216ee9b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Diagnosis, Computer-Assisted - methods</topic><topic>Electromyography - methods</topic><topic>Electromyography signal</topic><topic>Fatigue</topic><topic>Force</topic><topic>Fractal analysis</topic><topic>Fractals</topic><topic>Humans</topic><topic>Joint angle</topic><topic>Male</topic><topic>Muscle Contraction - physiology</topic><topic>Muscle, Skeletal - physiology</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Physical Medicine and Rehabilitation</topic><topic>Self-affinity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Talebinejad, Mehran</creatorcontrib><creatorcontrib>Chan, Adrian D.C</creatorcontrib><creatorcontrib>Miri, Ali</creatorcontrib><creatorcontrib>Dansereau, Richard M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of electromyography and kinesiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Talebinejad, Mehran</au><au>Chan, Adrian D.C</au><au>Miri, Ali</au><au>Dansereau, Richard M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fractal analysis of surface electromyography signals: A novel power spectrum-based method</atitle><jtitle>Journal of electromyography and kinesiology</jtitle><addtitle>J Electromyogr Kinesiol</addtitle><date>2009-10-01</date><risdate>2009</risdate><volume>19</volume><issue>5</issue><spage>840</spage><epage>850</epage><pages>840-850</pages><issn>1050-6411</issn><eissn>1873-5711</eissn><abstract>Abstract This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal’s interference pattern. These novel spectral indicators are capable of sensing the effects of motor units’ recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/ f α approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>18617420</pmid><doi>10.1016/j.jelekin.2008.05.004</doi><tpages>11</tpages></addata></record> |
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subjects | Adult Algorithms Diagnosis, Computer-Assisted - methods Electromyography - methods Electromyography signal Fatigue Force Fractal analysis Fractals Humans Joint angle Male Muscle Contraction - physiology Muscle, Skeletal - physiology Pattern Recognition, Automated - methods Physical Medicine and Rehabilitation Self-affinity |
title | Fractal analysis of surface electromyography signals: A novel power spectrum-based method |
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