JMIR Publications

ABSTRACT

Background:

Sleep apnea is a respiratory disorder characterized by frequent breathing cessation during sleep. Sleep apnea severity is determined by the apnea-hypopnea index (AHI), which is the hourly rate of respiratory events. In positional sleep apnea, the AHI is higher in the supine sleeping position than it is in other sleeping positions. Positional therapy is a behavioral strategy – e.g. wearing an item to encourage the sleeping position toward lateral – to treat positional apnea. The gold standard of diagnosing sleep apnea and whether it is positional or not is polysomnography, but this test is inconvenient, expensive, and has a long waiting list.

Objective:

The objective of this study was to develop and evaluate a non-contact method to estimate sleep apnea severity and to distinguish positional versus non-positional sleep apnea.

Methods:

In this paper, a non-contact deep learning algorithm was developed to analyze infrared video of sleep to estimate AHI and to distinguish patients with positional vs. non-positional sleep apnea. Specifically, a 3-dimensional convolutional neural network (3D-CNN) architecture was used to process movements extracted by optical flow to detect respiratory events. Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs. lateral) detected via a previously developed CNN model.

Results:

The algorithm was validated on data of 41 participants, age: 53±13 years, BMI: 30±7 kg/m2, 26 men and 15 women, AHI: 27±31 events/hour, sleep duration: 5±1 hour, 20 positional and 15 non-positional sleep apnea. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient of 0.79, p<0.001). Individuals with positional sleep apnea (based on the AHI=15 threshold) were identified with 83% accuracy and 86% F1-score.

Conclusions:

This paper showed the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, e.g. in the form of a tablet or phone application.