Detection of obstructive sleep apnea from wearable physiological devices

Apnea and hypopnea are common sleep disorders characterized by complete or partial obstructions of the airways, respectively. A sleep study, also known as polysomnography (PSG), is typically used to compute the Apnea-Hypopnea Index (AHI), the number of times a person has apnea or certain types of hypopnea per hour of sleep. AHI is then used to diagnose the severity of the sleep disorder. Early detection and treatment of apnea can significantly reduce morbidity and mortality. However, continuous PSG monitoring is unfeasible as it is costly and uncomfortable for patients. To circumvent these issues, we propose a method, named DRIVEN, to estimate AHI at home from wearable devices and assist physicians in diagnosing the severity of apneas. DRIVEN also detects when apnea, hypopnea, periods of wakefulness occur throughout the night, facilitating easy inspection by physicians. Patients can wear a single sensor or a combination of sensors that can be easily measured at home: abdominal movement, thoracic movement, or pulse oximetry. For example, using only two sensors, DRIVEN correctly classifies 72.4% of all test patients into one of the four AHI classes, with 99.3% either correctly classified or placed one class away from the true one. This is a reasonable trade-off between the models performance and patients comfort. We use data from three sleep studies from the National Sleep Research Resource (NSRR), the largest public repository, consisting of 14,370 recordings. DRIVEN is based on a combination of deep convolutional neural networks and a light-gradient-boost machine for classification. Since DRIVEN is simple and computationally efficient, it can be implemented for automatic estimation of AHI in unsupervised long-term home monitoring systems, reducing costs to healthcare systems and improving patient care.