Nasal pressure recordings for automatic snoring detection
This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway...
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| Veröffentlicht in: | Medical & biological engineering & computing 2015-11, Vol.53 (11), p.1103-1111 |
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| creator | Lee, Hyo-Ki Kim, Hojoong Lee, Kyoung-Joung |
| description | This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4 % and a positive predictive value of 92.0 % were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone. |
| doi_str_mv | 10.1007/s11517-015-1388-2 |
| format | Article |
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Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4 % and a positive predictive value of 92.0 % were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-015-1388-2</identifier><identifier>PMID: 26392181</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Acoustics ; Aged ; Algorithms ; Analysis ; Assessments ; Automation ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Body mass index ; Computer Applications ; Continuous positive airway pressure ; Cost analysis ; Female ; Human Physiology ; Humans ; Imaging ; Male ; Medical diagnosis ; Microphones ; Middle Aged ; Nose ; Nose - physiology ; Original Article ; Patients ; Polysomnography - methods ; Pressure ; Radiology ; Real time ; Recording ; Respiration ; Sensitivity and Specificity ; Sensors ; Signal Processing, Computer-Assisted ; Sleep ; Sleep apnea ; Sleep deprivation ; Sleep disorders ; Snoring - diagnosis ; Snoring - physiopathology ; Software ; Studies</subject><ispartof>Medical & biological engineering & computing, 2015-11, Vol.53 (11), p.1103-1111</ispartof><rights>International Federation for Medical and Biological Engineering 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c578t-466ec7bd5b1f679dc0d5b120b13e9e7198b2b133f38431bce3fe00e14cb0a4053</citedby><cites>FETCH-LOGICAL-c578t-466ec7bd5b1f679dc0d5b120b13e9e7198b2b133f38431bce3fe00e14cb0a4053</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-015-1388-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-015-1388-2$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26392181$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Hyo-Ki</creatorcontrib><creatorcontrib>Kim, Hojoong</creatorcontrib><creatorcontrib>Lee, Kyoung-Joung</creatorcontrib><title>Nasal pressure recordings for automatic snoring detection</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4 % and a positive predictive value of 92.0 % were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.</description><subject>Acoustics</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Assessments</subject><subject>Automation</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Body mass index</subject><subject>Computer Applications</subject><subject>Continuous positive airway pressure</subject><subject>Cost analysis</subject><subject>Female</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Male</subject><subject>Medical diagnosis</subject><subject>Microphones</subject><subject>Middle Aged</subject><subject>Nose</subject><subject>Nose - physiology</subject><subject>Original Article</subject><subject>Patients</subject><subject>Polysomnography - methods</subject><subject>Pressure</subject><subject>Radiology</subject><subject>Real time</subject><subject>Recording</subject><subject>Respiration</subject><subject>Sensitivity and Specificity</subject><subject>Sensors</subject><subject>Signal Processing, Computer-Assisted</subject><subject>Sleep</subject><subject>Sleep apnea</subject><subject>Sleep deprivation</subject><subject>Sleep disorders</subject><subject>Snoring - diagnosis</subject><subject>Snoring - physiopathology</subject><subject>Software</subject><subject>Studies</subject><issn>0140-0118</issn><issn>1741-0444</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkU1r3DAQhkVp6W42_QG9FEMvvTiZ0YclHcOSNIEluTRnYcvj4mXX2kj2If--MrstIVDYkwbp0TsaPYx9RbhCAH2dEBXqElCVKIwp-Qe2RC2xBCnlR7YElJBP0SzYRUpbAI6Ky89swSthORpcMvtYp3pXHCKlNEUqIvkQ2374nYouxKKexrCvx94XaQgxbxctjeTHPgyX7FNX7xJ9Oa0r9nx3-2t9X26efj6sbzalV9qMpawq8rppVYNdpW3rYS45NCjIkkZrGp5r0QkjBTaeREcAhNI3UEtQYsV-HHMPMbxMlEa375On3a4eKEzJodYgKgWSn4EKZTnP05-BciukBTAZ_f4O3YYpDnnmmTIKtEWZKTxSPoaUInXuEPt9HV8dgpttuaMtl2252Zab3_vtlDw1e2r_3firJwP8CKTD_PkU37T-b-ofgZKdEA</recordid><startdate>20151101</startdate><enddate>20151101</enddate><creator>Lee, 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pressure recordings for automatic snoring detection</title><author>Lee, Hyo-Ki ; Kim, Hojoong ; Lee, Kyoung-Joung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c578t-466ec7bd5b1f679dc0d5b120b13e9e7198b2b133f38431bce3fe00e14cb0a4053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acoustics</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Analysis</topic><topic>Assessments</topic><topic>Automation</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Body mass index</topic><topic>Computer Applications</topic><topic>Continuous positive airway pressure</topic><topic>Cost analysis</topic><topic>Female</topic><topic>Human Physiology</topic><topic>Humans</topic><topic>Imaging</topic><topic>Male</topic><topic>Medical diagnosis</topic><topic>Microphones</topic><topic>Middle 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One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><jtitle>Medical & biological engineering & computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lee, Hyo-Ki</au><au>Kim, Hojoong</au><au>Lee, Kyoung-Joung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nasal pressure recordings for automatic snoring detection</atitle><jtitle>Medical & biological engineering & computing</jtitle><stitle>Med Biol Eng Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2015-11-01</date><risdate>2015</risdate><volume>53</volume><issue>11</issue><spage>1103</spage><epage>1111</epage><pages>1103-1111</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>This study presents a rule-based method for automated, real-time snoring detection using nasal pressure recordings during overnight sleep. Although nasal pressure recordings provide information regarding nocturnal breathing abnormalities in a polysomnography (PSG) study or continuous positive airway pressure (CPAP) system, an objective assessment of snoring detection using these nasal pressure recordings has not yet been reported in the literature. Nasal pressure recordings were obtained from 55 patients with obstructive sleep apnea. The PSG data were also recorded simultaneously to evaluate the proposed method. This rule-based method for automatic, real-time snoring detection employed preprocessing, short-time energy and the central difference method. Using this methodology, a sensitivity of 85.4 % and a positive predictive value of 92.0 % were achieved in all patients. Therefore, we concluded that the proposed method is a simple, portable and cost-effective tool for real-time snoring detection in PSG and CPAP systems that does not require acoustic analysis using a microphone.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>26392181</pmid><doi>10.1007/s11517-015-1388-2</doi><tpages>9</tpages></addata></record> |
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| subjects | Acoustics Aged Algorithms Analysis Assessments Automation Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Body mass index Computer Applications Continuous positive airway pressure Cost analysis Female Human Physiology Humans Imaging Male Medical diagnosis Microphones Middle Aged Nose Nose - physiology Original Article Patients Polysomnography - methods Pressure Radiology Real time Recording Respiration Sensitivity and Specificity Sensors Signal Processing, Computer-Assisted Sleep Sleep apnea Sleep deprivation Sleep disorders Snoring - diagnosis Snoring - physiopathology Software Studies |
| title | Nasal pressure recordings for automatic snoring detection |
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