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.