Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors

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,...

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Bibliographic Details
Published in:IEEE transactions on biomedical circuits and systems 2018-08, Vol.12 (4), p.762-773
Main Authors: Surrel, Gregoire, Aminifar, Amir, Rincon, Francisco, Murali, Srinivasan, Atienza, David
Format: Article
Language:English
Subjects:
Apnea
Batteries
Biomedical monitoring
Cardiovascular diseases
EKG
Electrocardiography
Electrocardiography - methods
Embedded systems
Energy efficiency
Feature extraction
Heart diseases
Humans
Internet
Long-term monitoring
Medical devices
Medical electronics
Medical equipment
Monitoring
Monitoring, Ambulatory - methods
Obstructive Sleep Apnea (OSA)
Online detection
Polysomnography - methods
Real-time classification
Sleep
Sleep apnea
Sleep Apnea, Obstructive - diagnosis
Sleep disorders
Time domain analysis
Wearable Electronic Devices
Wearable sensor
Wearable sensors
Wearable technology
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Summary: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.
ISSN:1932-4545
1940-9990
DOI:10.1109/TBCAS.2018.2824659