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Conf Proc IEEE Eng Med Biol Soc. 2017 Jul;2017:2006-2009. doi: 10.1109/EMBC.2017.8037245.

Screening patients for risk of sleep apnea using facial photographs.


We investigated using frontal and profile facial photographic images for screening patients for risk of sleep apnea. A 180 image pairs were used from patients who were diagnosed using an attended overnight polysomnogram test into controls (AHI<;10/h) and sleep apnea (AHIā‰„10/h). A series of 35 landmarks and 71 features motivated by craniofacial structure pertinent to upper airway physiology were identified on the photographs. After reducing the dimension of the feature set using recursive feature selection, the features were processed by a Support Vector Machine (SVM). Classification was performed using linear kernel SVM. The accuracy and area under Receiver Operating Curve (ROC) improved when the number of features reduced from 71 to eight top-ranked features. Further improvement was achieved by adding clinical measurements to the selected features resulting in the accuracy of 80% and the area under ROC of 0.83.

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