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 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.
Knorr-Chung
Bethany R
BR
Thayer School of Engineering, Dartmouth College, Hanover, USA. brknorrchung@ieee.org
McGrath
Susan P
SP
Blike
George T
GT
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