Computerized analysis of snoring in Sleep Apnea Syndrome
The International Classification of Sleep Disorders lists 90 disorders. Manifestations, such as snoring, are important signs in the diagnosis of the Obstructive Sleep Apnea Syndrome; they are also socially undesirable. The aim of this paper was to present and evaluate a computerized tool that automa...
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| Veröffentlicht in: | Brazilian journal of otorhinolaryngology 2011-07, Vol.77 (4), p.488-498 |
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| container_title | Brazilian journal of otorhinolaryngology |
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| creator | Shiomi, Fabio Koiti Pisa, Ivan Torres de Campos, Carlos José Reis |
| description | The International Classification of Sleep Disorders lists 90 disorders. Manifestations, such as snoring, are important signs in the diagnosis of the Obstructive Sleep Apnea Syndrome; they are also socially undesirable.
The aim of this paper was to present and evaluate a computerized tool that automatically identifies snoring and highlights the importance of establishing the duration of each snoring event in OSA patients.
The low-sampling (200 Hz) electrical signal that indicates snoring was measured during polysomnography. The snoring sound of 31 patients was automatically classified by the software. The Kappa approach was applied to measure agreement between the automatic detection software and a trained observer. Student's T test was applied to evaluate differences in the duration of snoring episodes among simple snorers and OSA snorers.
Of a total 43,976 snoring episodes, the software sensitivity was 99.26%, the specificity was 97.35%, and Kappa was 0.96. We found a statistically significant difference (p |
| doi_str_mv | 10.1590/S1808-86942011000400013 |
| format | Article |
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The aim of this paper was to present and evaluate a computerized tool that automatically identifies snoring and highlights the importance of establishing the duration of each snoring event in OSA patients.
The low-sampling (200 Hz) electrical signal that indicates snoring was measured during polysomnography. The snoring sound of 31 patients was automatically classified by the software. The Kappa approach was applied to measure agreement between the automatic detection software and a trained observer. Student's T test was applied to evaluate differences in the duration of snoring episodes among simple snorers and OSA snorers.
Of a total 43,976 snoring episodes, the software sensitivity was 99.26%, the specificity was 97.35%, and Kappa was 0.96. We found a statistically significant difference (p <0.0001) in the duration of snoring episodes (simple snoring x OSA snorers).
This computer software makes it easier to generate quantitative reports of snoring, thereby reducing manual labor.</description><identifier>ISSN: 1808-8694</identifier><identifier>ISSN: 1808-8686</identifier><identifier>EISSN: 1808-8686</identifier><identifier>DOI: 10.1590/S1808-86942011000400013</identifier><identifier>PMID: 21860976</identifier><language>eng</language><publisher>Brazil: Elsevier Editora Ltda</publisher><subject>Adolescent ; Adult ; Algorithms ; apnea ; Child ; Child, Preschool ; decision support techniques ; Female ; Humans ; information systems ; Male ; Middle Aged ; Observer Variation ; Original ; OTORHINOLARYNGOLOGY ; Polysomnography - methods ; Reproducibility of Results ; Sensitivity and Specificity ; Signal Processing, Computer-Assisted ; sleep apnea syndromes ; Sleep Apnea Syndromes - complications ; Sleep Apnea Syndromes - diagnosis ; snoring ; Snoring - diagnosis ; Snoring - etiology ; Young Adult</subject><ispartof>Brazilian journal of otorhinolaryngology, 2011-07, Vol.77 (4), p.488-498</ispartof><rights>2011 Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial</rights><rights>This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c513t-603b546ef5800490108c2f62861c00f5be7c591c55b9735ab5b1ffddb3891fb73</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9450794/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9450794/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21860976$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shiomi, Fabio Koiti</creatorcontrib><creatorcontrib>Pisa, Ivan Torres</creatorcontrib><creatorcontrib>de Campos, Carlos José Reis</creatorcontrib><title>Computerized analysis of snoring in Sleep Apnea Syndrome</title><title>Brazilian journal of otorhinolaryngology</title><addtitle>Braz J Otorhinolaryngol</addtitle><description>The International Classification of Sleep Disorders lists 90 disorders. Manifestations, such as snoring, are important signs in the diagnosis of the Obstructive Sleep Apnea Syndrome; they are also socially undesirable.
The aim of this paper was to present and evaluate a computerized tool that automatically identifies snoring and highlights the importance of establishing the duration of each snoring event in OSA patients.
The low-sampling (200 Hz) electrical signal that indicates snoring was measured during polysomnography. The snoring sound of 31 patients was automatically classified by the software. The Kappa approach was applied to measure agreement between the automatic detection software and a trained observer. Student's T test was applied to evaluate differences in the duration of snoring episodes among simple snorers and OSA snorers.
Of a total 43,976 snoring episodes, the software sensitivity was 99.26%, the specificity was 97.35%, and Kappa was 0.96. We found a statistically significant difference (p <0.0001) in the duration of snoring episodes (simple snoring x OSA snorers).
This computer software makes it easier to generate quantitative reports of snoring, thereby reducing manual labor.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Algorithms</subject><subject>apnea</subject><subject>Child</subject><subject>Child, Preschool</subject><subject>decision support techniques</subject><subject>Female</subject><subject>Humans</subject><subject>information systems</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Observer Variation</subject><subject>Original</subject><subject>OTORHINOLARYNGOLOGY</subject><subject>Polysomnography - methods</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Signal Processing, Computer-Assisted</subject><subject>sleep apnea syndromes</subject><subject>Sleep Apnea Syndromes - complications</subject><subject>Sleep Apnea Syndromes - diagnosis</subject><subject>snoring</subject><subject>Snoring - diagnosis</subject><subject>Snoring - etiology</subject><subject>Young Adult</subject><issn>1808-8694</issn><issn>1808-8686</issn><issn>1808-8686</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFUVtr2zAUFqNlvWx_YfNbn5IdxdbtZRBCtxYKfUj7LGT5qFOxJU-yC-mvr9NkYWWwPggJznfTdwj5SmFOmYJvaypBziRX1QIoBYBqOrT8QE73A8mPDm9VnZCznB8BuADBPpKTBZUclOCnRK5i148DJv-MTWGCaTfZ5yK6IoeYfHgofCjWLWJfLPuAplhvQpNih5_IsTNtxs_7-5zc_7i8W13Nbm5_Xq-WNzPLaDnMOJQ1qzg6JqeQCihIu3B8ITm1AI7VKCxT1DJWK1EyU7OaOtc0dSkVdbUoz8l8p5utxzbqxzimKWXWrxXofyqYCN93hH6sO2wshiGZVvfJdyZtdDRev50E_0s_xCetKgZCVZPAxV4gxd8j5kF3PltsWxMwjllLyYCxSmytxA5pU8w5oTu4UNDbPf0n5Je_Qx54fxYzAZY7AE7dPnlMevv_YLHxCe2gm-jfNXkB8dSfdg</recordid><startdate>20110701</startdate><enddate>20110701</enddate><creator>Shiomi, Fabio Koiti</creator><creator>Pisa, Ivan Torres</creator><creator>de Campos, Carlos José Reis</creator><general>Elsevier Editora Ltda</general><general>Elsevier</general><general>Associação Brasileira de Otorrinolaringologia e Cirurgia Cérvico-Facial</general><scope>6I.</scope><scope>AAFTH</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><scope>5PM</scope><scope>GPN</scope></search><sort><creationdate>20110701</creationdate><title>Computerized analysis of snoring in Sleep Apnea Syndrome</title><author>Shiomi, Fabio Koiti ; Pisa, Ivan Torres ; de Campos, Carlos José Reis</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c513t-603b546ef5800490108c2f62861c00f5be7c591c55b9735ab5b1ffddb3891fb73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Algorithms</topic><topic>apnea</topic><topic>Child</topic><topic>Child, Preschool</topic><topic>decision support techniques</topic><topic>Female</topic><topic>Humans</topic><topic>information systems</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Observer Variation</topic><topic>Original</topic><topic>OTORHINOLARYNGOLOGY</topic><topic>Polysomnography - methods</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Signal Processing, Computer-Assisted</topic><topic>sleep apnea syndromes</topic><topic>Sleep Apnea Syndromes - complications</topic><topic>Sleep Apnea Syndromes - diagnosis</topic><topic>snoring</topic><topic>Snoring - diagnosis</topic><topic>Snoring - etiology</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shiomi, Fabio Koiti</creatorcontrib><creatorcontrib>Pisa, Ivan Torres</creatorcontrib><creatorcontrib>de Campos, Carlos José Reis</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</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><collection>PubMed Central (Full Participant titles)</collection><collection>SciELO</collection><jtitle>Brazilian journal of otorhinolaryngology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shiomi, Fabio Koiti</au><au>Pisa, Ivan Torres</au><au>de Campos, Carlos José Reis</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Computerized analysis of snoring in Sleep Apnea Syndrome</atitle><jtitle>Brazilian journal of otorhinolaryngology</jtitle><addtitle>Braz J Otorhinolaryngol</addtitle><date>2011-07-01</date><risdate>2011</risdate><volume>77</volume><issue>4</issue><spage>488</spage><epage>498</epage><pages>488-498</pages><issn>1808-8694</issn><issn>1808-8686</issn><eissn>1808-8686</eissn><abstract>The International Classification of Sleep Disorders lists 90 disorders. Manifestations, such as snoring, are important signs in the diagnosis of the Obstructive Sleep Apnea Syndrome; they are also socially undesirable.
The aim of this paper was to present and evaluate a computerized tool that automatically identifies snoring and highlights the importance of establishing the duration of each snoring event in OSA patients.
The low-sampling (200 Hz) electrical signal that indicates snoring was measured during polysomnography. The snoring sound of 31 patients was automatically classified by the software. The Kappa approach was applied to measure agreement between the automatic detection software and a trained observer. Student's T test was applied to evaluate differences in the duration of snoring episodes among simple snorers and OSA snorers.
Of a total 43,976 snoring episodes, the software sensitivity was 99.26%, the specificity was 97.35%, and Kappa was 0.96. We found a statistically significant difference (p <0.0001) in the duration of snoring episodes (simple snoring x OSA snorers).
This computer software makes it easier to generate quantitative reports of snoring, thereby reducing manual labor.</abstract><cop>Brazil</cop><pub>Elsevier Editora Ltda</pub><pmid>21860976</pmid><doi>10.1590/S1808-86942011000400013</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adolescent Adult Algorithms apnea Child Child, Preschool decision support techniques Female Humans information systems Male Middle Aged Observer Variation Original OTORHINOLARYNGOLOGY Polysomnography - methods Reproducibility of Results Sensitivity and Specificity Signal Processing, Computer-Assisted sleep apnea syndromes Sleep Apnea Syndromes - complications Sleep Apnea Syndromes - diagnosis snoring Snoring - diagnosis Snoring - etiology Young Adult |
| title | Computerized analysis of snoring in Sleep Apnea Syndrome |
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