Identifying airway obstructions using photoplethysmography (PPG).
Objective: Central and obstructive apneas are sources of morbidity and mortality associated with primary patient conditions as well as secondary to medical care such as sedation/analgesia in post-operative patients. This research investigates the predictive value of the respirophasic variation in th...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 22; no. 2; pp. 95 - 102 |
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| Autores principales: | , , , , , |
| Formato: | clinical trial research Journal Article |
| Publicado: |
Springer Nature
Apr2008
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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=105808638&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105808638 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Apr2008 vid: 22 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105808638 32620282 NLM18219579 2009891470 10.1007/s10877-008-9110-7 NLM18219579 105808638 ppf: 95 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying airway obstructions using photoplethysmography (PPG). aug: au: Knorr-Chung BR McGrath SP Blike GT Knorr-Chung, Bethany R McGrath, Susan P Blike, George T affil: Thayer School of Engineering, Dartmouth College, Hanover, USA sug: subj: Airway Obstruction Diagnosis Plethysmography Signal Processing, Computer Assisted Adult Airway Obstruction Physiopathology Anesthesia, General Adverse Effects Clinical Trials Female Male Monitoring, Physiologic Methods Neural Networks (Computer) Postoperative Period Predictive Value of Tests Sleep Apnea, Obstructive Diagnosis Sleep Apnea, Obstructive Physiopathology Human Adult: 19-44 years Female Male ab: Objective: Central and obstructive apneas are sources of morbidity and mortality associated with primary patient conditions as well as secondary to medical care such as sedation/analgesia in post-operative patients. This research investigates the predictive value of the respirophasic variation in the noninvasive photoplethysmography (PPG) waveform signal in detecting airway obstruction.Methods: PPG data from 20 consenting healthy adults (12 male, 8 female) undergoing anesthesia were collected directly after surgery and before transfer to the Post Anesthesia Care Unit (PACU). Features of the PPG waveform were calculated and used in a neural network to classify normal and obstructive events.Results: During the postoperative period studied, the neural network classifier yielded an average (+/-standard deviation) 75.4 (+/-3.7)% sensitivity, 91.6 (+/-2.3)% specificity, 84.7 (+/-3.5)% positive predictive value, 85.9 (+/-1.8)% negative predictive value, and an overall accuracy of 85.4 (+/-2.0)%.Conclusions: The accuracy of this method shows promise for use in real-time monitoring situations. pubtype: Academic Journal doctype: clinical trial research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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