Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera.
Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report h...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 34; no. 5; pp. 1025 - 1034 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145302265&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145302265 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Oct2020 vid: 34 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 145302265 145302265 NLM31701371 10.1007/s10877-019-00417-6 NLM31701371 145302265 ppf: 1025 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera. aug: au: Addison, Paul S. Smit, Philip Jacquel, Dominique Borg, Ulf R. affil: Medtronic, Video Biosignals Group, Patient Monitoring, Technopole Centre, EH26 0PJ, Edinburgh, UK sug: subj: Oximetry Respiratory Rate Capnography Monitoring, Physiologic ab: Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RRdepth). This was compared to a reference respiratory rate determined from a capnograph (RRcap). The bias and root mean squared difference (RMSD) accuracy between RRdepth and the reference RRcap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RRdepth = 0.99 × RRcap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RRdepth and RRcap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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