Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison of non-parametric and parametric spectral estimation techniques
Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, th...
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| Veröffentlicht in: | Neurophysiologie clinique 2009-04, Vol.39 (2), p.107-115 |
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| creator | Amodio, P Orsato, R Marchetti, P Schiff, S Poci, C Angeli, P Gatta, A Sparacino, G Toffolo, G.M |
| description | Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands. |
| doi_str_mv | 10.1016/j.neucli.2009.02.002 |
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Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.</description><identifier>ISSN: 0987-7053</identifier><identifier>EISSN: 1769-7131</identifier><identifier>DOI: 10.1016/j.neucli.2009.02.002</identifier><identifier>PMID: 19467441</identifier><language>eng</language><publisher>Paris: Elsevier SAS</publisher><subject>Adult ; Algorithms ; Analyse spectrale ; Autoregressive model ; Biological and medical sciences ; Cirrhose ; Cirrhosis ; EEG ; Electroencephalography - methods ; Encéphalopathie hépatique ; Female ; FFT ; Fourier Analysis ; Hepatic encephalopathy ; Hepatic Encephalopathy - blood ; Hepatic Encephalopathy - diagnosis ; Hepatic Encephalopathy - etiology ; Hepatic Encephalopathy - physiopathology ; Human viral diseases ; Humans ; Infectious diseases ; Liver Cirrhosis - complications ; Liver Cirrhosis - physiopathology ; Male ; Medical sciences ; Middle Aged ; Modèle autorégressif ; Neurology ; Prognosis ; Severity of Illness Index ; Spectral analysis ; Spectrum Analysis ; Statistics, Nonparametric ; Viral diseases ; Viral diseases of the nervous system</subject><ispartof>Neurophysiologie clinique, 2009-04, Vol.39 (2), p.107-115</ispartof><rights>Elsevier Masson SAS</rights><rights>2009 Elsevier Masson SAS</rights><rights>2009 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c445t-86392420acaabe8998c641e07b0fbd9667021fb0d36bd9c872bcbaebca1037753</citedby><cites>FETCH-LOGICAL-c445t-86392420acaabe8998c641e07b0fbd9667021fb0d36bd9c872bcbaebca1037753</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.neucli.2009.02.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,782,786,3552,27931,27932,46002</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=21480546$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/19467441$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Amodio, P</creatorcontrib><creatorcontrib>Orsato, R</creatorcontrib><creatorcontrib>Marchetti, P</creatorcontrib><creatorcontrib>Schiff, S</creatorcontrib><creatorcontrib>Poci, C</creatorcontrib><creatorcontrib>Angeli, P</creatorcontrib><creatorcontrib>Gatta, A</creatorcontrib><creatorcontrib>Sparacino, G</creatorcontrib><creatorcontrib>Toffolo, G.M</creatorcontrib><title>Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison of non-parametric and parametric spectral estimation techniques</title><title>Neurophysiologie clinique</title><addtitle>Neurophysiol Clin</addtitle><description>Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.</description><subject>Adult</subject><subject>Algorithms</subject><subject>Analyse spectrale</subject><subject>Autoregressive model</subject><subject>Biological and medical sciences</subject><subject>Cirrhose</subject><subject>Cirrhosis</subject><subject>EEG</subject><subject>Electroencephalography - methods</subject><subject>Encéphalopathie hépatique</subject><subject>Female</subject><subject>FFT</subject><subject>Fourier Analysis</subject><subject>Hepatic encephalopathy</subject><subject>Hepatic Encephalopathy - blood</subject><subject>Hepatic Encephalopathy - diagnosis</subject><subject>Hepatic Encephalopathy - etiology</subject><subject>Hepatic Encephalopathy - physiopathology</subject><subject>Human viral diseases</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Liver Cirrhosis - complications</subject><subject>Liver Cirrhosis - physiopathology</subject><subject>Male</subject><subject>Medical sciences</subject><subject>Middle Aged</subject><subject>Modèle autorégressif</subject><subject>Neurology</subject><subject>Prognosis</subject><subject>Severity of Illness Index</subject><subject>Spectral analysis</subject><subject>Spectrum Analysis</subject><subject>Statistics, Nonparametric</subject><subject>Viral diseases</subject><subject>Viral diseases of the nervous system</subject><issn>0987-7053</issn><issn>1769-7131</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2009</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFklGL1DAUhYMo7uzqPxDpi751vEkzTeuDsAy7Kiz4oIJvIU1vbcY2qbmtMD_Ff2vqDKv44lM48J1zwz2XsWccthx4-eqw9bjYwW0FQL0FsQUQD9iGq7LOFS_4Q7aBulK5gl1xwS6JDgAgi7p4zC54LUslJd-wnzcD2jkG9Ban3gzhazRT72xmvBmO5CjrQszmHjNDhEQj-jkLXdbjZOaE3fuS7I-vs30YJxMdBb9SPvg8STPiHH9nttlfkqZ1tBkypNmNKS6ZZrS9d98XpCfsUWcGwqfn94p9vr35tH-X3314-35_fZdbKXdzXpVFLaQAY41psKrrypaSI6gGuqaty1KB4F0DbVEmaSslGtsYbKzhUCi1K67Yy1PuFMM6d9ajI4vDYDyGhXSphKqgqhIoT6CNgShip6eYvh2PmoNeK9EHfapEr5VoEDpVkmzPz_lLM2L7x3TuIAEvzoAha4YuGm8d3XOCywp2skzcmxOHaRs_HEZN1q37b11Mi9RtcP_7yb8BifAuzfyGR6RDWGIqnTTXlAz643o-6_VAnS6nEF-KX7PHxfE</recordid><startdate>20090401</startdate><enddate>20090401</enddate><creator>Amodio, P</creator><creator>Orsato, R</creator><creator>Marchetti, P</creator><creator>Schiff, S</creator><creator>Poci, C</creator><creator>Angeli, P</creator><creator>Gatta, A</creator><creator>Sparacino, G</creator><creator>Toffolo, G.M</creator><general>Elsevier SAS</general><general>Elsevier</general><scope>IQODW</scope><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>20090401</creationdate><title>Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison of non-parametric and parametric spectral estimation techniques</title><author>Amodio, P ; Orsato, R ; Marchetti, P ; Schiff, S ; Poci, C ; Angeli, P ; Gatta, A ; Sparacino, G ; Toffolo, G.M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c445t-86392420acaabe8998c641e07b0fbd9667021fb0d36bd9c872bcbaebca1037753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Adult</topic><topic>Algorithms</topic><topic>Analyse spectrale</topic><topic>Autoregressive model</topic><topic>Biological and medical sciences</topic><topic>Cirrhose</topic><topic>Cirrhosis</topic><topic>EEG</topic><topic>Electroencephalography - methods</topic><topic>Encéphalopathie hépatique</topic><topic>Female</topic><topic>FFT</topic><topic>Fourier Analysis</topic><topic>Hepatic encephalopathy</topic><topic>Hepatic Encephalopathy - blood</topic><topic>Hepatic Encephalopathy - diagnosis</topic><topic>Hepatic Encephalopathy - etiology</topic><topic>Hepatic Encephalopathy - physiopathology</topic><topic>Human viral diseases</topic><topic>Humans</topic><topic>Infectious diseases</topic><topic>Liver Cirrhosis - complications</topic><topic>Liver Cirrhosis - physiopathology</topic><topic>Male</topic><topic>Medical sciences</topic><topic>Middle Aged</topic><topic>Modèle autorégressif</topic><topic>Neurology</topic><topic>Prognosis</topic><topic>Severity of Illness Index</topic><topic>Spectral analysis</topic><topic>Spectrum Analysis</topic><topic>Statistics, Nonparametric</topic><topic>Viral diseases</topic><topic>Viral diseases of the nervous system</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Amodio, P</creatorcontrib><creatorcontrib>Orsato, R</creatorcontrib><creatorcontrib>Marchetti, P</creatorcontrib><creatorcontrib>Schiff, S</creatorcontrib><creatorcontrib>Poci, C</creatorcontrib><creatorcontrib>Angeli, P</creatorcontrib><creatorcontrib>Gatta, A</creatorcontrib><creatorcontrib>Sparacino, G</creatorcontrib><creatorcontrib>Toffolo, G.M</creatorcontrib><collection>Pascal-Francis</collection><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>Neurophysiologie clinique</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Amodio, P</au><au>Orsato, R</au><au>Marchetti, P</au><au>Schiff, S</au><au>Poci, C</au><au>Angeli, P</au><au>Gatta, A</au><au>Sparacino, G</au><au>Toffolo, G.M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison of non-parametric and parametric spectral estimation techniques</atitle><jtitle>Neurophysiologie clinique</jtitle><addtitle>Neurophysiol Clin</addtitle><date>2009-04-01</date><risdate>2009</risdate><volume>39</volume><issue>2</issue><spage>107</spage><epage>115</epage><pages>107-115</pages><issn>0987-7053</issn><eissn>1769-7131</eissn><abstract>Summary Objective To compare electroencephalographic spectral analysis obtained by periodogram (calculated by means of Fast Fourier Transform) and autoregressive (AR) modelling for the assessment of hepatic encephalopathy. Methods The mean dominant frequency (MDF) and the relative power of delta, theta, alpha, and beta bands were computed by both techniques from the electroencephalograms (EEG) of 201 cirrhotics and were evaluated in the clinical and prognostic assessment of the patients. Results The values of all the five indexes computed by periodogram and AR modelling matched each other, but the latter provided stable values after the analysis of fewer epochs. Independently of the technique, the relative power of theta and alpha bands fitted the clinical data and had prognostic value. The relative power of beta and delta bands computed by AR modelling fitted more closely with clinical data fitted the clinical data more closely. Conclusions The electroencephalographic spectral indexes obtained by periodogram and AR modelling were found to be, on average, undistinguishable, but the latter appeared less sensitive to noise and provided a more reliable assessment of low-power bands.</abstract><cop>Paris</cop><pub>Elsevier SAS</pub><pmid>19467441</pmid><doi>10.1016/j.neucli.2009.02.002</doi><tpages>9</tpages></addata></record> |
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| subjects | Adult Algorithms Analyse spectrale Autoregressive model Biological and medical sciences Cirrhose Cirrhosis EEG Electroencephalography - methods Encéphalopathie hépatique Female FFT Fourier Analysis Hepatic encephalopathy Hepatic Encephalopathy - blood Hepatic Encephalopathy - diagnosis Hepatic Encephalopathy - etiology Hepatic Encephalopathy - physiopathology Human viral diseases Humans Infectious diseases Liver Cirrhosis - complications Liver Cirrhosis - physiopathology Male Medical sciences Middle Aged Modèle autorégressif Neurology Prognosis Severity of Illness Index Spectral analysis Spectrum Analysis Statistics, Nonparametric Viral diseases Viral diseases of the nervous system |
| title | Electroencephalographic analysis for the assessment of hepatic encephalopathy: Comparison of non-parametric and parametric spectral estimation techniques |
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