title: Identifying airway obstructions using photoplethysmography (PPG).
abstract: 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.
authors.affiliation: Thayer School of Engineering, Dartmouth College, Hanover, USA. brknorrchung@ieee.org
authors.forename: Bethany R
authors.initials: BR
authors.lastname: Knorr-Chung
authors.forename: Susan P
authors.initials: SP
authors.lastname: McGrath
authors.forename: George T
authors.initials: GT
authors.lastname: Blike
mesh_terms: D000328:Adult; D000402:Airway Obstruction / Q000175:diagnosis* / Q000503:physiopathology; D000768:Anesthesia, General / Q000009:adverse effects; D005260:Female; D006801:Humans; D008297:Male; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D017156:Photoplethysmography; D011184:Postoperative Period; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis / Q000503:physiopathology