Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors
Surrel, Gregoire ; Aminifar, Amir LU
; Rincon, Francisco
; Murali, Srinivasan
and Atienza, David
(2018)
In IEEE Transactions on Biomedical Circuits and Systems
12(4).
p.762-773
- Abstract
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... (More)
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.
(Less)
- author
- Surrel, Gregoire
; Aminifar, Amir
LU
; Rincon, Francisco
; Murali, Srinivasan
and Atienza, David
- publishing date
- 2018-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Electrocardiography/methods, Humans, Monitoring, Ambulatory/methods, Polysomnography/methods, Sleep Apnea, Obstructive/diagnosis, Wearable Electronic Devices
- in
- IEEE Transactions on Biomedical Circuits and Systems
- volume
- 12
- issue
- 4
- pages
- 12 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- external identifiers
-
- pmid:29993894
- scopus:85046463514
- ISSN
- 1932-4545
- DOI
- 10.1109/TBCAS.2018.2824659
- language
- English
- LU publication?
- no
- id
- 3c001fc5-2dd2-42d8-8a5c-51a4c59426ea
- date added to LUP
- 2021-08-31 15:27:52
- date last changed
- 2024-06-16 18:04:31
@article{3c001fc5-2dd2-42d8-8a5c-51a4c59426ea,
abstract = {{<p>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.</p>}},
author = {{Surrel, Gregoire and Aminifar, Amir and Rincon, Francisco and Murali, Srinivasan and Atienza, David}},
issn = {{1932-4545}},
keywords = {{Electrocardiography/methods; Humans; Monitoring, Ambulatory/methods; Polysomnography/methods; Sleep Apnea, Obstructive/diagnosis; Wearable Electronic Devices}},
language = {{eng}},
number = {{4}},
pages = {{762--773}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{IEEE Transactions on Biomedical Circuits and Systems}},
title = {{Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors}},
url = {{http://dx.doi.org/10.1109/TBCAS.2018.2824659}},
doi = {{10.1109/TBCAS.2018.2824659}},
volume = {{12}},
year = {{2018}},
}