Tidal Volume Level Estimation Using Respiratory Sounds
Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels durin...
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Veröffentlicht in: | Journal of healthcare engineering 2023, Vol.2023 (1), p.4994668 |
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description | Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO2) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness. |
doi_str_mv | 10.1155/2023/4994668 |
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However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO2) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness.</description><identifier>ISSN: 2040-2295</identifier><identifier>ISSN: 2040-2309</identifier><identifier>EISSN: 2040-2309</identifier><identifier>DOI: 10.1155/2023/4994668</identifier><identifier>PMID: 36844947</identifier><language>eng</language><publisher>England: Hindawi</publisher><subject>Algorithms ; Humans ; Respiratory Sounds ; Sleep Apnea, Obstructive ; Snoring ; Tidal Volume</subject><ispartof>Journal of healthcare engineering, 2023, Vol.2023 (1), p.4994668</ispartof><rights>Copyright © 2023 Lurui Wang and Zhongwei Jiang.</rights><rights>Copyright © 2023 Lurui Wang and Zhongwei Jiang. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4018-8e05021ef81da1cb4e90d267fa5a17704ac33603364b1991f99fb54ee7f8ef953</citedby><cites>FETCH-LOGICAL-c4018-8e05021ef81da1cb4e90d267fa5a17704ac33603364b1991f99fb54ee7f8ef953</cites><orcidid>0000-0003-3772-4814 ; 0000-0002-4701-9812</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949945/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9949945/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,729,782,786,887,4026,27930,27931,27932,53798,53800</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36844947$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Zhang, Yatao</contributor><creatorcontrib>Wang, Lurui</creatorcontrib><creatorcontrib>Jiang, Zhongwei</creatorcontrib><title>Tidal Volume Level Estimation Using Respiratory Sounds</title><title>Journal of healthcare engineering</title><addtitle>J Healthc Eng</addtitle><description>Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO2) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness.</description><subject>Algorithms</subject><subject>Humans</subject><subject>Respiratory Sounds</subject><subject>Sleep Apnea, Obstructive</subject><subject>Snoring</subject><subject>Tidal Volume</subject><issn>2040-2295</issn><issn>2040-2309</issn><issn>2040-2309</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>EIF</sourceid><recordid>eNp9kF1LwzAUhoMobszdeS29FLQuadI2uRFkzA8YCLp5G9L2ZIu0zUzayf69HftAbwwnJHAe3nN4ELok-I6QOB5FOKIjJgRLEn6C-hFmOIwoFqeHfyTiHhp6_4m7QwVlhJ6jHk04Y4KlfZTMTKHK4MOWbQXBFNZQBhPfmEo1xtbB3Jt6EbyBXxmnGus2wbtt68JfoDOtSg_D_TtA88fJbPwcTl-fXsYP0zBnmPCQA45xREBzUiiSZwwELqIk1SpWJE0xUzmlCe4uy4gQRAuhs5gBpJqDFjEdoPtd7qrNKihyqBunSrly3YJuI60y8m-nNku5sGvZKelqG3C9D3D2qwXfyMr4HMpS1WBbL6OUY8YTymiH3u7Q3FnvHejjGILl1rbc2pZ72x1-9Xu1I3xw2wE3O2Bp6kJ9m__jfgAH5Iaf</recordid><startdate>2023</startdate><enddate>2023</enddate><creator>Wang, Lurui</creator><creator>Jiang, Zhongwei</creator><general>Hindawi</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</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><orcidid>https://orcid.org/0000-0003-3772-4814</orcidid><orcidid>https://orcid.org/0000-0002-4701-9812</orcidid></search><sort><creationdate>2023</creationdate><title>Tidal Volume Level Estimation Using Respiratory Sounds</title><author>Wang, Lurui ; Jiang, Zhongwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4018-8e05021ef81da1cb4e90d267fa5a17704ac33603364b1991f99fb54ee7f8ef953</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Algorithms</topic><topic>Humans</topic><topic>Respiratory Sounds</topic><topic>Sleep Apnea, Obstructive</topic><topic>Snoring</topic><topic>Tidal Volume</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Lurui</creatorcontrib><creatorcontrib>Jiang, Zhongwei</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing 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><jtitle>Journal of healthcare engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Lurui</au><au>Jiang, Zhongwei</au><au>Zhang, Yatao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tidal Volume Level Estimation Using Respiratory Sounds</atitle><jtitle>Journal of healthcare engineering</jtitle><addtitle>J Healthc Eng</addtitle><date>2023</date><risdate>2023</risdate><volume>2023</volume><issue>1</issue><spage>4994668</spage><pages>4994668-</pages><issn>2040-2295</issn><issn>2040-2309</issn><eissn>2040-2309</eissn><abstract>Respiratory sounds have been used as a noninvasive and convenient method to estimate respiratory flow and tidal volume. However, current methods need calibration, making them difficult to use in a home environment. A respiratory sound analysis method is proposed to estimate tidal volume levels during sleep qualitatively. Respiratory sounds are filtered and segmented into one-minute clips, all clips are clustered into three categories: normal breathing/snoring/uncertain with agglomerative hierarchical clustering (AHC). Formant parameters are extracted to classify snoring clips into simple snoring and obstructive snoring with the K-means algorithm. For simple snoring clips, the tidal volume level is calculated based on snoring last time. For obstructive snoring clips, the tidal volume level is calculated by the maximum breathing pause interval. The performance of the proposed method is evaluated on an open dataset, PSG-Audio, in which full-night polysomnography (PSG) and tracheal sound were recorded simultaneously. The calculated tidal volume levels are compared with the corresponding lowest nocturnal oxygen saturation (LoO2) data. Experiments show that the proposed method calculates tidal volume levels with high accuracy and robustness.</abstract><cop>England</cop><pub>Hindawi</pub><pmid>36844947</pmid><doi>10.1155/2023/4994668</doi><orcidid>https://orcid.org/0000-0003-3772-4814</orcidid><orcidid>https://orcid.org/0000-0002-4701-9812</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Humans Respiratory Sounds Sleep Apnea, Obstructive Snoring Tidal Volume |
title | Tidal Volume Level Estimation Using Respiratory Sounds |
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