Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds
To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA). Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of a...
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| creator | Dafna, Eliran Tarasiuk, Ariel Zigel, Yaniv |
| description | To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).
Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity.
University affiliated sleep-wake disorder center and biomedical signal processing laboratory.
One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects.
Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively.
This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances. |
| doi_str_mv | 10.1371/journal.pone.0117382 |
| format | Article |
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Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity.
University affiliated sleep-wake disorder center and biomedical signal processing laboratory.
One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects.
Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively.
This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0117382</identifier><identifier>PMID: 25710495</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acoustics ; Adult ; Aged ; Aged, 80 and over ; Algorithms ; Biomedical engineering ; Body mass ; Breathing ; Data processing ; Design ; Electrocardiography ; Electromyography ; Engineering ; Evaluation ; Female ; Humans ; Laboratories ; Latency ; Likelihood Functions ; Machine learning ; Male ; Middle Aged ; Night ; Parameter estimation ; Patients ; Pattern recognition ; Physiology ; Polysomnography ; Quality ; Respiration ; Respiratory Sounds - physiology ; Sensitivity ; Sensors ; Signal processing ; Sleep ; Sleep and wakefulness ; Sleep apnea ; Sleep disorders ; Studies ; Tape Recording ; Wakefulness ; Young Adult</subject><ispartof>PloS one, 2015-02, Vol.10 (2), p.e0117382-e0117382</ispartof><rights>2015 Dafna et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Dafna et al 2015 Dafna et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c592t-3d24bd76cb8d14dfe0cd8c20c787f0324b072189af662208d836d535a1ba41d93</citedby><cites>FETCH-LOGICAL-c592t-3d24bd76cb8d14dfe0cd8c20c787f0324b072189af662208d836d535a1ba41d93</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339734/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4339734/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25710495$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>McLoughlin, Ian</contributor><creatorcontrib>Dafna, Eliran</creatorcontrib><creatorcontrib>Tarasiuk, Ariel</creatorcontrib><creatorcontrib>Zigel, Yaniv</creatorcontrib><title>Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).
Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity.
University affiliated sleep-wake disorder center and biomedical signal processing laboratory.
One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects.
Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively.
This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.</description><subject>Acoustics</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Algorithms</subject><subject>Biomedical engineering</subject><subject>Body mass</subject><subject>Breathing</subject><subject>Data processing</subject><subject>Design</subject><subject>Electrocardiography</subject><subject>Electromyography</subject><subject>Engineering</subject><subject>Evaluation</subject><subject>Female</subject><subject>Humans</subject><subject>Laboratories</subject><subject>Latency</subject><subject>Likelihood Functions</subject><subject>Machine learning</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Night</subject><subject>Parameter estimation</subject><subject>Patients</subject><subject>Pattern recognition</subject><subject>Physiology</subject><subject>Polysomnography</subject><subject>Quality</subject><subject>Respiration</subject><subject>Respiratory Sounds - physiology</subject><subject>Sensitivity</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Sleep</subject><subject>Sleep and wakefulness</subject><subject>Sleep apnea</subject><subject>Sleep disorders</subject><subject>Studies</subject><subject>Tape Recording</subject><subject>Wakefulness</subject><subject>Young Adult</subject><issn>1932-6203</issn><issn>1932-6203</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><sourceid>DOA</sourceid><recordid>eNptUk1v1DAQtRCIloV_gCASFy5Z_Bk7FyRU8VGpEgdAHK2J7exm8XoWO2nFvyftplWLOHk8896bDz1CXjK6ZkKzdzuccoK4PmAKa8qYFoY_IqesFbxuOBWP78Un5FkpO0qVME3zlJxwpRmVrTolP7_FEA71FfwKVbiEOME4YKr6jPvqaosx1GnYbMcqYaodphHcWMHkB6xycJj9kDalwr7qcoBxO_-qglPy5Tl50kMs4cXyrsiPTx-_n32pL75-Pj_7cFE71fKxFp7LzuvGdcYz6ftAnTeOU6eN7qmYi1RzZlrom4ZzarwRjVdCAetAMt-KFXl91D1ELHa5SbGsUYYqI5SaEedHhEfY2UMe9pD_WITB3iQwbyzkcXAxWK3Acd5TLTsndeCGg4S5tQDtZD_Lrcj7pdvU7YN3IY0Z4gPRh5U0bO0GL60UotVCzgJvF4GMv6dQRrsfigsxQgo43czd6sbIls3QN_9A_7-dPKJcxlJy6O-GYdRe--SWZa99YhefzLRX9xe5I90aQ_wFMi28hw</recordid><startdate>20150224</startdate><enddate>20150224</enddate><creator>Dafna, Eliran</creator><creator>Tarasiuk, Ariel</creator><creator>Zigel, Yaniv</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><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>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20150224</creationdate><title>Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds</title><author>Dafna, Eliran ; Tarasiuk, Ariel ; Zigel, Yaniv</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c592t-3d24bd76cb8d14dfe0cd8c20c787f0324b072189af662208d836d535a1ba41d93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Acoustics</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Algorithms</topic><topic>Biomedical engineering</topic><topic>Body mass</topic><topic>Breathing</topic><topic>Data processing</topic><topic>Design</topic><topic>Electrocardiography</topic><topic>Electromyography</topic><topic>Engineering</topic><topic>Evaluation</topic><topic>Female</topic><topic>Humans</topic><topic>Laboratories</topic><topic>Latency</topic><topic>Likelihood Functions</topic><topic>Machine learning</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Night</topic><topic>Parameter estimation</topic><topic>Patients</topic><topic>Pattern recognition</topic><topic>Physiology</topic><topic>Polysomnography</topic><topic>Quality</topic><topic>Respiration</topic><topic>Respiratory Sounds - physiology</topic><topic>Sensitivity</topic><topic>Sensors</topic><topic>Signal processing</topic><topic>Sleep</topic><topic>Sleep and wakefulness</topic><topic>Sleep apnea</topic><topic>Sleep disorders</topic><topic>Studies</topic><topic>Tape Recording</topic><topic>Wakefulness</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dafna, Eliran</creatorcontrib><creatorcontrib>Tarasiuk, Ariel</creatorcontrib><creatorcontrib>Zigel, Yaniv</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dafna, Eliran</au><au>Tarasiuk, Ariel</au><au>Zigel, Yaniv</au><au>McLoughlin, Ian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-02-24</date><risdate>2015</risdate><volume>10</volume><issue>2</issue><spage>e0117382</spage><epage>e0117382</epage><pages>e0117382-e0117382</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>To develop and validate a novel non-contact system for whole-night sleep evaluation using breathing sounds analysis (BSA).
Whole-night breathing sounds (using ambient microphone) and polysomnography (PSG) were simultaneously collected at a sleep laboratory (mean recording time 7.1 hours). A set of acoustic features quantifying breathing pattern were developed to distinguish between sleep and wake epochs (30 sec segments). Epochs (n = 59,108 design study and n = 68,560 validation study) were classified using AdaBoost classifier and validated epoch-by-epoch for sensitivity, specificity, positive and negative predictive values, accuracy, and Cohen's kappa. Sleep quality parameters were calculated based on the sleep/wake classifications and compared with PSG for validity.
University affiliated sleep-wake disorder center and biomedical signal processing laboratory.
One hundred and fifty patients (age 54.0±14.8 years, BMI 31.6±5.5 kg/m2, m/f 97/53) referred for PSG were prospectively and consecutively recruited. The system was trained (design study) on 80 subjects; validation study was blindly performed on the additional 70 subjects.
Epoch-by-epoch accuracy rate for the validation study was 83.3% with sensitivity of 92.2% (sleep as sleep), specificity of 56.6% (awake as awake), and Cohen's kappa of 0.508. Comparing sleep quality parameters of BSA and PSG demonstrate average error of sleep latency, total sleep time, wake after sleep onset, and sleep efficiency of 16.6 min, 35.8 min, and 29.6 min, and 8%, respectively.
This study provides evidence that sleep-wake activity and sleep quality parameters can be reliably estimated solely using breathing sound analysis. This study highlights the potential of this innovative approach to measure sleep in research and clinical circumstances.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>25710495</pmid><doi>10.1371/journal.pone.0117382</doi><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Acoustics Adult Aged Aged, 80 and over Algorithms Biomedical engineering Body mass Breathing Data processing Design Electrocardiography Electromyography Engineering Evaluation Female Humans Laboratories Latency Likelihood Functions Machine learning Male Middle Aged Night Parameter estimation Patients Pattern recognition Physiology Polysomnography Quality Respiration Respiratory Sounds - physiology Sensitivity Sensors Signal processing Sleep Sleep and wakefulness Sleep apnea Sleep disorders Studies Tape Recording Wakefulness Young Adult |
| title | Sleep-wake evaluation from whole-night non-contact audio recordings of breathing sounds |
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