ROC Analysis and a Realistic Model of Heart Rate Variability
We have carried out a pilot study on a standard collection of electrocardiograms from patients who suffer from congestive heart failure, and subjects without cardiac pathology, using receiver-operating-characteristic (ROC) analysis. The scale-dependent wavelet-coefficient standard deviation \sigma_{...
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| creator | Thurner, Stefan Feurstein, Markus C Teich, Malvin C |
| description | We have carried out a pilot study on a standard collection of
electrocardiograms from patients who suffer from congestive heart failure, and
subjects without cardiac pathology, using receiver-operating-characteristic
(ROC) analysis. The scale-dependent wavelet-coefficient standard deviation
\sigma_{wav}(m), a multiresolution-based analysis measure, is found to be
superior to two commonly used measures of cardiac dysfunction when the two
classes of patients cannot be completely separated. A jittered
integrate-and-fire model with a fractal Gaussian-noise kernel provides a
realistic simulation of heartbeat sequences for both heart-failure patients and
normal subjects. |
| doi_str_mv | 10.48550/arxiv.chao-dyn/9806022 |
| format | Article |
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electrocardiograms from patients who suffer from congestive heart failure, and
subjects without cardiac pathology, using receiver-operating-characteristic
(ROC) analysis. The scale-dependent wavelet-coefficient standard deviation
\sigma_{wav}(m), a multiresolution-based analysis measure, is found to be
superior to two commonly used measures of cardiac dysfunction when the two
classes of patients cannot be completely separated. A jittered
integrate-and-fire model with a fractal Gaussian-noise kernel provides a
realistic simulation of heartbeat sequences for both heart-failure patients and
normal subjects.</description><identifier>DOI: 10.48550/arxiv.chao-dyn/9806022</identifier><language>eng</language><subject>Physics - Chaotic Dynamics ; Quantitative Biology - Biomolecules ; Quantitative Biology - Cell Behavior ; Quantitative Biology - Genomics ; Quantitative Biology - Molecular Networks ; Quantitative Biology - Neurons and Cognition ; Quantitative Biology - Other ; Quantitative Biology - Populations and Evolution ; Quantitative Biology - Quantitative Methods ; Quantitative Biology - Subcellular Processes ; Quantitative Biology - Tissues and Organs</subject><creationdate>1998-06</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/chao-dyn/9806022$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.chao-dyn/9806022$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Thurner, Stefan</creatorcontrib><creatorcontrib>Feurstein, Markus C</creatorcontrib><creatorcontrib>Teich, Malvin C</creatorcontrib><title>ROC Analysis and a Realistic Model of Heart Rate Variability</title><description>We have carried out a pilot study on a standard collection of
electrocardiograms from patients who suffer from congestive heart failure, and
subjects without cardiac pathology, using receiver-operating-characteristic
(ROC) analysis. The scale-dependent wavelet-coefficient standard deviation
\sigma_{wav}(m), a multiresolution-based analysis measure, is found to be
superior to two commonly used measures of cardiac dysfunction when the two
classes of patients cannot be completely separated. A jittered
integrate-and-fire model with a fractal Gaussian-noise kernel provides a
realistic simulation of heartbeat sequences for both heart-failure patients and
normal subjects.</description><subject>Physics - Chaotic Dynamics</subject><subject>Quantitative Biology - Biomolecules</subject><subject>Quantitative Biology - Cell Behavior</subject><subject>Quantitative Biology - Genomics</subject><subject>Quantitative Biology - Molecular Networks</subject><subject>Quantitative Biology - Neurons and Cognition</subject><subject>Quantitative Biology - Other</subject><subject>Quantitative Biology - Populations and Evolution</subject><subject>Quantitative Biology - Quantitative Methods</subject><subject>Quantitative Biology - Subcellular Processes</subject><subject>Quantitative Biology - Tissues and Organs</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1998</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz0FrwjAYxvFcdhhun2G5eKymSRMT8CJlmwNFKOK1vCbvi4HYjrSM9dvPbZ6ewx8e-DH2UopFZbUWS8jf8WvhL9AXYeqWzgojpHxk6-ZQ800HaRriwKELHHiDkOIwRs_3fcDEe-JbhDzyBkbkJ8gRzjHFcXpiDwRpwOf7ztjx7fVYb4vd4f2j3uwKWDlZyIBohNZAylpDRnljra6gwjP5W1ASwAWHRKIClF4Ya2TpnbElWWdIzdj8__YP0X7meIU8tb-Y9oZp7xj1AwuiRsI</recordid><startdate>19980619</startdate><enddate>19980619</enddate><creator>Thurner, Stefan</creator><creator>Feurstein, Markus C</creator><creator>Teich, Malvin C</creator><scope>ALC</scope><scope>GOX</scope></search><sort><creationdate>19980619</creationdate><title>ROC Analysis and a Realistic Model of Heart Rate Variability</title><author>Thurner, Stefan ; Feurstein, Markus C ; Teich, Malvin C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a792-2dee6055af3886f63c68854a4ebfc60532aa9d9eff04ae2c068621c9681f896f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Physics - Chaotic Dynamics</topic><topic>Quantitative Biology - Biomolecules</topic><topic>Quantitative Biology - Cell Behavior</topic><topic>Quantitative Biology - Genomics</topic><topic>Quantitative Biology - Molecular Networks</topic><topic>Quantitative Biology - Neurons and Cognition</topic><topic>Quantitative Biology - Other</topic><topic>Quantitative Biology - Populations and Evolution</topic><topic>Quantitative Biology - Quantitative Methods</topic><topic>Quantitative Biology - Subcellular Processes</topic><topic>Quantitative Biology - Tissues and Organs</topic><toplevel>online_resources</toplevel><creatorcontrib>Thurner, Stefan</creatorcontrib><creatorcontrib>Feurstein, Markus C</creatorcontrib><creatorcontrib>Teich, Malvin C</creatorcontrib><collection>arXiv Quantitative Biology</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Thurner, Stefan</au><au>Feurstein, Markus C</au><au>Teich, Malvin C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>ROC Analysis and a Realistic Model of Heart Rate Variability</atitle><date>1998-06-19</date><risdate>1998</risdate><abstract>We have carried out a pilot study on a standard collection of
electrocardiograms from patients who suffer from congestive heart failure, and
subjects without cardiac pathology, using receiver-operating-characteristic
(ROC) analysis. The scale-dependent wavelet-coefficient standard deviation
\sigma_{wav}(m), a multiresolution-based analysis measure, is found to be
superior to two commonly used measures of cardiac dysfunction when the two
classes of patients cannot be completely separated. A jittered
integrate-and-fire model with a fractal Gaussian-noise kernel provides a
realistic simulation of heartbeat sequences for both heart-failure patients and
normal subjects.</abstract><doi>10.48550/arxiv.chao-dyn/9806022</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Physics - Chaotic Dynamics Quantitative Biology - Biomolecules Quantitative Biology - Cell Behavior Quantitative Biology - Genomics Quantitative Biology - Molecular Networks Quantitative Biology - Neurons and Cognition Quantitative Biology - Other Quantitative Biology - Populations and Evolution Quantitative Biology - Quantitative Methods Quantitative Biology - Subcellular Processes Quantitative Biology - Tissues and Organs |
| title | ROC Analysis and a Realistic Model of Heart Rate Variability |
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