Quantum mechanics

Intensive care medicine

Obstructive sleep apnea

Embedded system

Remote patient monitoring

Operating system

Cardiology

Computer science

Radiology

Sleep (system call)

Ambulatory

Physics

Wearable computer

Sleep apnea

Apnea

Internal medicine

Medicine

Term (time)

IEEE Transactions on Biomedical Circuits and Systems

Obstructive Sleep Apnea (OSA) is one of the main under-diagnosed sleep disorder. It is an aggravating factor for several serious cardiovascular diseases, including stroke. There is, however, a lack of medical devices for long-term ambulatory monitoring of OSA since current systems are rather bulky, expensive, intrusive, and cannot be used for long-term monitoring in ambulatory settings. In this paper, we propose a wearable, accurate, and energy efficient system for monitoring obstructive sleep apnea on a long-term basis. As an embedded system for Internet of Things, it reduces the gap between home health-care and professional supervision. Our approach is based on monitoring the patient using a single-channel electrocardiogram signal. We develop an efficient time-domain analysis to meet the stringent resources constraints of embedded systems to compute the sleep apnea score. Our system, for a publicly available database (PhysioNet Apnea-ECG), has a classification accuracy of up to 88.2% for our new online and patient-specific analysis, which takes the distinct profile of each patient into account. While accurate, our approach is also energy efficient and can achieve a battery lifetime of 46 days for continuous screening of OSA.

Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors