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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A comparative analysis of spectral exponent estimation techniques for 1/<italic>f</italic><sup>β</sup> processes with applications to the analysis of stride interval time series</title><author><name sortKey="Schaefer, Alexander" sort="Schaefer, Alexander" uniqKey="Schaefer A" first="Alexander" last="Schaefer">Alexander Schaefer</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Brach, Jennifer S" sort="Brach, Jennifer S" uniqKey="Brach J" first="Jennifer S." last="Brach">Jennifer S. Brach</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Perera, Subashan" sort="Perera, Subashan" uniqKey="Perera S" first="Subashan" last="Perera">Subashan Perera</name><affiliation><nlm:aff id="A3">Department of Medicine, Division of Geriatrics, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Sejdi, Ervin" sort="Sejdi, Ervin" uniqKey="Sejdi E" first="Ervin" last="Sejdi">Ervin Sejdi</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">24200509</idno><idno type="pmc">3947294</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3947294</idno><idno type="RBID">PMC:3947294</idno><idno type="doi">10.1016/j.jneumeth.2013.10.017</idno><date when="2013">2013</date><idno type="wicri:Area/Pmc/Corpus">000261</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000261</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A comparative analysis of spectral exponent estimation techniques for 1/<italic>f</italic><sup>β</sup> processes with applications to the analysis of stride interval time series</title><author><name sortKey="Schaefer, Alexander" sort="Schaefer, Alexander" uniqKey="Schaefer A" first="Alexander" last="Schaefer">Alexander Schaefer</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Brach, Jennifer S" sort="Brach, Jennifer S" uniqKey="Brach J" first="Jennifer S." last="Brach">Jennifer S. Brach</name><affiliation><nlm:aff id="A2">Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</nlm:aff></affiliation></author><author><name sortKey="Perera, Subashan" sort="Perera, Subashan" uniqKey="Perera S" first="Subashan" last="Perera">Subashan Perera</name><affiliation><nlm:aff id="A3">Department of Medicine, Division of Geriatrics, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author><author><name sortKey="Sejdi, Ervin" sort="Sejdi, Ervin" uniqKey="Sejdi E" first="Ervin" last="Sejdi">Ervin Sejdi</name><affiliation><nlm:aff id="A1">Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Journal of neuroscience methods</title><idno type="ISSN">0165-0270</idno><idno type="eISSN">1872-678X</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec id="S1"><title>Background</title><p id="P1">The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum <italic>S</italic>(<italic>f</italic>) = 1/<italic>f</italic><sup>β</sup>. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline.</p></sec><sec id="S2"><title>New Method</title><p id="P2">This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared.</p></sec><sec id="S3"><title>Results</title><p id="P3">The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances.</p></sec><sec id="S4"><title>Conclusions</title><p id="P4">The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.</p></sec></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">7905558</journal-id><journal-id journal-id-type="pubmed-jr-id">5306</journal-id><journal-id journal-id-type="nlm-ta">J Neurosci Methods</journal-id><journal-id journal-id-type="iso-abbrev">J. Neurosci. Methods</journal-id><journal-title-group><journal-title>Journal of neuroscience methods</journal-title></journal-title-group><issn pub-type="ppub">0165-0270</issn><issn pub-type="epub">1872-678X</issn></journal-meta><article-meta><article-id pub-id-type="pmid">24200509</article-id><article-id pub-id-type="pmc">3947294</article-id><article-id pub-id-type="doi">10.1016/j.jneumeth.2013.10.017</article-id><article-id pub-id-type="manuscript">NIHMS538432</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>A comparative analysis of spectral exponent estimation techniques for 1/<italic>f</italic><sup>β</sup> processes with applications to the analysis of stride interval time series</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Schaefer</surname><given-names>Alexander</given-names></name><xref ref-type="aff" rid="A1">a</xref></contrib><contrib contrib-type="author"><name><surname>Brach</surname><given-names>Jennifer S.</given-names></name><xref ref-type="aff" rid="A2">b</xref></contrib><contrib contrib-type="author"><name><surname>Perera</surname><given-names>Subashan</given-names></name><xref ref-type="aff" rid="A3">c</xref></contrib><contrib contrib-type="author"><name><surname>Sejdić</surname><given-names>Ervin</given-names></name><xref ref-type="aff" rid="A1">a</xref><xref ref-type="corresp" rid="cor1">*</xref></contrib></contrib-group><aff id="A1"><label>a</label>Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA</aff><aff id="A2"><label>b</label>Department of Physical Therapy, University of Pittsburgh, Pittsburgh, PA, 15260, USA</aff><aff id="A3"><label>c</label>Department of Medicine, Division of Geriatrics, University of Pittsburgh, Pittsburgh, PA, 15261, USA</aff><author-notes><corresp id="cor1"><label>*</label>Corresponding author. <email>esejdic@ieee.org</email></corresp></author-notes><pub-date pub-type="nihms-submitted"><day>24</day><month>12</month><year>2013</year></pub-date><pub-date pub-type="epub"><day>04</day><month>11</month><year>2013</year></pub-date><pub-date pub-type="ppub"><day>30</day><month>1</month><year>2014</year></pub-date><pub-date pub-type="pmc-release"><day>30</day><month>1</month><year>2015</year></pub-date><volume>222</volume><fpage>118</fpage><lpage>130</lpage><pmc-comment>elocation-id from pubmed: 10.1016/j.jneumeth.2013.10.017</pmc-comment>
<permissions><copyright-statement>© 2013 Elsevier B.V. All rights reserved.</copyright-statement><copyright-year>2013</copyright-year></permissions><abstract><sec id="S1"><title>Background</title><p id="P1">The time evolution and complex interactions of many nonlinear systems, such as in the human body, result in fractal types of parameter outcomes that exhibit self similarity over long time scales by a power law in the frequency spectrum <italic>S</italic>(<italic>f</italic>) = 1/<italic>f</italic><sup>β</sup>. The scaling exponent β is thus often interpreted as a “biomarker” of relative health and decline.</p></sec><sec id="S2"><title>New Method</title><p id="P2">This paper presents a thorough comparative numerical analysis of fractal characterization techniques with specific consideration given to experimentally measured gait stride interval time series. The ideal fractal signals generated in the numerical analysis are constrained under varying lengths and biases indicative of a range of physiologically conceivable fractal signals. This analysis is to complement previous investigations of fractal characteristics in healthy and pathological gait stride interval time series, with which this study is compared.</p></sec><sec id="S3"><title>Results</title><p id="P3">The results of our analysis showed that the averaged wavelet coefficient method consistently yielded the most accurate results. Comparison with Existing Methods: Class dependent methods proved to be unsuitable for physiological time series. Detrended fluctuation analysis as most prevailing method in the literature exhibited large estimation variances.</p></sec><sec id="S4"><title>Conclusions</title><p id="P4">The comparative numerical analysis and experimental applications provide a thorough basis for determining an appropriate and robust method for measuring and comparing a physiologically meaningful biomarker, the spectral index β. In consideration of the constraints of application, we note the significant drawbacks of detrended fluctuation analysis and conclude that the averaged wavelet coefficient method can provide reasonable consistency and accuracy for characterizing these fractal time series.</p></sec></abstract><kwd-group><kwd>fractals</kwd><kwd>time series analysis</kwd><kwd>self similarity</kwd><kwd>gait</kwd><kwd>stride intervals</kwd><kwd>detrended fluctuation analysis</kwd><kwd>wavelets</kwd><kwd>1/f process</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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