Non-contact Apnea-Hypopnea Index Estimation using Near Infrared Video

Sleep apnea is a highly prevalent and underdiagnosed sleep disorder characterized by repeated intermittent interruptions to breathing. Sleep apnea severity is measured with the apnea-hypopnea index (AHI), defined as the number of apnea or hypopnea events per hour of sleep. We hypothesize that respiratory related motion features extracted from infrared video can be used to reliably estimate AHI. The 3 feature variables chosen for apneic event estimation, and separately for sleep versus awake estimation, were: the estimated respiratory rate, the magnitude of respiratory movement, and the amount of movements. Leave-one-person-out cross validation on data from 19 participants was used to train and test a random forest binary classifier to detect apneas and hypopneas. Linear regression of the number of estimated events over estimated sleep duration and the total duration of estimated apneic events over estimated sleep duration was used to estimate AHI. Sleeping versus awake segments was estimated with mean ± standard deviation accuracy of 76.0% ± 17.7%. AHI was estimated with correlation coefficient of 0.76 (p <; 0.01) to the clinical gold standard AHI. Accuracy of 78.9% was achieved for classifying AHI ≥ 15, with sensitivity of 70.0%, specificity of 88.9%, and precision of 87.5%. Motion features extracted from infrared video are concluded to be suitable for estimation of AHI.