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Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device
: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting...
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Published in: | Journal of clinical medicine 2024-12, Vol.13 (23), p.7199 |
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description | : Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies.
: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study.
: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively.
: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR. |
doi_str_mv | 10.3390/jcm13237199 |
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: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study.
: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively.
: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.</description><identifier>ISSN: 2077-0383</identifier><identifier>EISSN: 2077-0383</identifier><identifier>DOI: 10.3390/jcm13237199</identifier><identifier>PMID: 39685655</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Chronic obstructive pulmonary disease ; Data collection ; FDA approval ; Health aspects ; Methods ; Patients ; Performance evaluation ; Physiology ; Pulmonary function tests ; Sensors ; Signal processing ; Sleep apnea ; Software ; Usability ; Vital signs</subject><ispartof>Journal of clinical medicine, 2024-12, Vol.13 (23), p.7199</ispartof><rights>COPYRIGHT 2024 MDPI AG</rights><rights>2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2024 by the authors. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-9558-5520 ; 0000-0001-6616-1841 ; 0000-0002-2232-5880</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3144190976/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3144190976?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,38516,43895,44590,53791,53793,74412,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39685655$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Abdulsadig, Rawan S</creatorcontrib><creatorcontrib>Devani, Nikesh</creatorcontrib><creatorcontrib>Singh, Sukhpreet</creatorcontrib><creatorcontrib>Patel, Zaibaa</creatorcontrib><creatorcontrib>Pramono, Renard Xaviero Adhi</creatorcontrib><creatorcontrib>Mandal, Swapna</creatorcontrib><creatorcontrib>Rodriguez-Villegas, Esther</creatorcontrib><title>Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device</title><title>Journal of clinical medicine</title><addtitle>J Clin Med</addtitle><description>: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies.
: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study.
: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively.
: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.</description><subject>Accuracy</subject><subject>Chronic obstructive pulmonary disease</subject><subject>Data collection</subject><subject>FDA approval</subject><subject>Health aspects</subject><subject>Methods</subject><subject>Patients</subject><subject>Performance evaluation</subject><subject>Physiology</subject><subject>Pulmonary function tests</subject><subject>Sensors</subject><subject>Signal processing</subject><subject>Sleep apnea</subject><subject>Software</subject><subject>Usability</subject><subject>Vital signs</subject><issn>2077-0383</issn><issn>2077-0383</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>COVID</sourceid><sourceid>PIMPY</sourceid><recordid>eNptUV1rFDEUDaLYsu2T7xLwxZet-c7kSZa12kKh0A99jEnmzphlJlkzs4X-e7O0aismDwn3nHtuTg5Cbyg54dyQD5swUs64psa8QIeMaL0kvOEvn9wP0PE0bUhdTSMY1a_RATeqkUrKQ_R9PcQUgxvwVzfE1s0xJ5w7fAXTNhY353KPr9wM-HSa4_gA304x9XgV8q7WAr6OfXLDhLuSR-zwN3DF-QHwJ7iLAY7Qq66icPx4LtDt59Ob9dny4vLL-Xp1sew5M_Oyc7SRnDJqWi8oFcQEqUznBXSNCcIxpX3wUgsJDgh44lpmfKs9A-6kV3yBPj7obnd-hDZAmosb7LbUV5d7m120z5EUf9g-31lKlWCiDl-g948KJf_cwTTbMU4BhsElqFYtp0IZqozQlfruH-om78r-F_YsQQ0xWv1l9W4AG1OX6-CwF7WrpgYmWc2ksk7-w6q7hTGGnKCLtf6s4e1Tp38s_g6V_wKMFaNf</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Abdulsadig, Rawan S</creator><creator>Devani, Nikesh</creator><creator>Singh, Sukhpreet</creator><creator>Patel, Zaibaa</creator><creator>Pramono, Renard Xaviero Adhi</creator><creator>Mandal, Swapna</creator><creator>Rodriguez-Villegas, Esther</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-9558-5520</orcidid><orcidid>https://orcid.org/0000-0001-6616-1841</orcidid><orcidid>https://orcid.org/0000-0002-2232-5880</orcidid></search><sort><creationdate>20241201</creationdate><title>Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device</title><author>Abdulsadig, Rawan S ; Devani, Nikesh ; Singh, Sukhpreet ; Patel, Zaibaa ; Pramono, Renard Xaviero Adhi ; Mandal, Swapna ; Rodriguez-Villegas, Esther</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-g329t-fa18531219db411409c569fb4ef89c4a267bcb5745eae0eb0ad29bd7b2e3a5b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Chronic obstructive pulmonary disease</topic><topic>Data collection</topic><topic>FDA approval</topic><topic>Health aspects</topic><topic>Methods</topic><topic>Patients</topic><topic>Performance evaluation</topic><topic>Physiology</topic><topic>Pulmonary function tests</topic><topic>Sensors</topic><topic>Signal processing</topic><topic>Sleep apnea</topic><topic>Software</topic><topic>Usability</topic><topic>Vital signs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abdulsadig, Rawan S</creatorcontrib><creatorcontrib>Devani, Nikesh</creatorcontrib><creatorcontrib>Singh, Sukhpreet</creatorcontrib><creatorcontrib>Patel, Zaibaa</creatorcontrib><creatorcontrib>Pramono, Renard Xaviero Adhi</creatorcontrib><creatorcontrib>Mandal, Swapna</creatorcontrib><creatorcontrib>Rodriguez-Villegas, Esther</creatorcontrib><collection>PubMed</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdulsadig, Rawan S</au><au>Devani, Nikesh</au><au>Singh, Sukhpreet</au><au>Patel, Zaibaa</au><au>Pramono, Renard Xaviero Adhi</au><au>Mandal, Swapna</au><au>Rodriguez-Villegas, Esther</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device</atitle><jtitle>Journal of clinical medicine</jtitle><addtitle>J Clin Med</addtitle><date>2024-12-01</date><risdate>2024</risdate><volume>13</volume><issue>23</issue><spage>7199</spage><pages>7199-</pages><issn>2077-0383</issn><eissn>2077-0383</eissn><abstract>: Respiratory rate (RR) is a clinical measure of breathing frequency, a vital metric for clinical assessment. However, the recording and documentation of RR are considered to be extremely poor due to the limitations of the current approaches to measuring RR, including capnography and manual counting. We conducted a validation of the automatic RR measurement capability of AcuPebble RE100 (Acurable, London, UK) against a gold-standard capnography system and a type-III cardiorespiratory polygraphy system in two independent prospective and retrospective studies.
: The experiment for the prospective study was conducted at Imperial College London. Data from AcuPebble RE100 (Acurable, London, UK) and the reference capnography system (Capnostream™35, Medtronic, Minneapolis, MN, USA) were collected simultaneously from healthy volunteers. The data from a previously published study were used in the retrospective study, where the patients were recruited consecutively from a standard Obstructive Sleep Apnea (OSA) diagnostic pathway in a UK hospital. Overnight data during sleep were collected using the AcuPebble SA100 (Acurable, London, UK) sensor and a type-III cardiorespiratory polygraphy system (Embletta MPR Sleep System, Natus Medical, Pleasanton, CA, USA) at the patients' homes. Data from 15 healthy volunteers were used in the prospective study. For the retrospective study, 150 consecutive patients had been referred for OSA diagnosis and successfully completed the study.
: The RR output of AcuPebble RE100 (Acurable, London, UK) was compared against the reference device in terms of the Root Mean Squared Deviation (RMSD), mean error, and standard deviation (SD) of the difference between the paired measurements. In both the prospective and retrospective studies, the AcuPebble RE100 algorithms provided accurate RR measurements, well within the clinically relevant margin of error, typically used by FDA-approved respiratory rate monitoring devices, with the RMSD under three breaths per minute (BPM) and mean errors of 1.83 BPM and 1.4 BPM, respectively.
: The evaluation results provide evidence that AcuPebble RE100 (Acurable, London, UK) algorithms produce reliable results and are hence suitable for overnight monitoring of RR.</abstract><cop>Switzerland</cop><pub>MDPI AG</pub><pmid>39685655</pmid><doi>10.3390/jcm13237199</doi><orcidid>https://orcid.org/0000-0001-9558-5520</orcidid><orcidid>https://orcid.org/0000-0001-6616-1841</orcidid><orcidid>https://orcid.org/0000-0002-2232-5880</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Accuracy Chronic obstructive pulmonary disease Data collection FDA approval Health aspects Methods Patients Performance evaluation Physiology Pulmonary function tests Sensors Signal processing Sleep apnea Software Usability Vital signs |
title | Clinical Validation of Respiratory Rate Estimation Using Acoustic Signals from a Wearable Device |
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