research-article

Free access

A body area network-based detection of sleep apnea

Authors:

Sheryl LaFleur,

Imad MahgoubAuthors Info & Claims

BodyNets '14: Proceedings of the 9th International Conference on Body Area Networks

Pages 165 - 169

https://doi.org/10.4108/icst.bodynets.2014.258205

Published: 29 September 2014 Publication History

PDF eReader

Abstract

Presented in this paper are the analysis and the results of our body area network (BAN) for the detection and classification of obstructive sleep apnea. Our algorithm is based on a low-order Daubechies D4 and D6 hybrid filter. Feature extractions are taken from the QT intervals of the Electrocardiography (ECG) waveform. These features include a QT-ECG-derived respiratory rate (QT-EDR) and the instantaneous heart rate. A determination of the existence of sleep apnea is based on the variability of these features.

References

[1]

LaFleur, Sheryl, and Imad Mahgoub. "A personal body area network as a pre-screening surrogate to the polysomnography." Proceedings of the 8th International Conference on Body Area Networks. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2013.

Digital Library

Google Scholar

[2]

Darpo, B., Nebout, T. and Sager, P. T. (2006), Clinical Evaluation of QT/QTc Prolongation and Proarrhythmic Potential for Nonantiarrhythmic Drugs: The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use E14 Guideline. Journal of Clinical Pharma, 46: 498--507.

Crossref

Google Scholar

[3]

Moody GB, Mark RG, Zoccola A, Vergani L. Derivation of respiratory signals from multilead ECGs. Computers in Cardiology, IEEE 1985; 12: 113--116.

Google Scholar

[4]

Moody GB, Mark RG, Zoccola A, Mantero S. Clinical validation of the ECG-derived respiration (QT-EDR) technique. In: Computers in Cardiology, Piscataway NJ: IEEE Computer Society Press; 1986: 507--10.

Google Scholar

[5]

Lipsitz LA, Hashimoto F, Lubowsky LP, Mietus JE, Moody GB, Appenzeller O, Goldberger AL. Heart rate and respiratory rhythm dynamics on ascent to high altitude. Br Heart J 1995; 74: 390--396.

Google Scholar

[6]

Bsoul, M. Minn, H. Tamil, L., Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG, Information Technology in Biomedicine, IEEE Transactions, May 2011, Volume 15, Issue 3, pp. 416--427.

Digital Library

Google Scholar

[7]

Mahmoodabadi, S. Z., A. Ahmadian, and M. D. Abolhasani. "ECG feature extraction using Daubechies wavelets." Proceedings of the fifth IASTED International conference on Visualization, Imaging and Image Processing. 2005.

Google Scholar

[8]

"Target Heart Rates." Target Heart Rates. Americam Heart Association, 22 Mar. 2013. Web. 11 June 2014.

Google Scholar

[9]

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23): e215--e220 {Circulation Electronic Pages; http://circ.ahajournals.org/cgi/content/full/101/23/e215}; 2000 (June 13).

Google Scholar

[10]

"Simple Regression." Simple Regression. N.p., n.d. Web. 18 Aug. 2014.

Google Scholar

Index Terms

A body area network-based detection of sleep apnea
1. Software and its engineering
  1. Software notations and tools
    1. Development frameworks and environments
      1. Integrated and visual development environments
    2. General programming languages
      1. Language types

Recommendations

Nocturnal evolution of heart rate variability indices in sleep apnea

Heart rate variability (HRV) is a valuable clinical tool in diagnosing multiple diseases. This paper presents the results of a spectral HRV analysis conducted with 46 patients. HRV indices for the whole night show differences among patients with severe ...
Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings
Special section on body sensor networks

Obstructive sleep apnea or hypopnea causes a pause or reduction in airflow with continuous breathing effort. The aim of this study is to identify individual apnea and hypopnea events from normal breathing events using wavelet-based features of 5-s ECG ...
An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings
Highlights
► DWT based analysis of EKG recordings. ► FFT based feature extraction. ► Feature space reduction. ► LS-SVM based classification.

Abstract
Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder. The traditional diagnosis methods of the disorder are cumbersome and expensive. The ability to automatically identify OSA from electrocardiogram (ECG) recordings is ...

Comments

Information & Contributors

Information

Published In

BodyNets '14: Proceedings of the 9th International Conference on Body Area Networks

September 2014

385 pages

ISBN:9781631900471

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 29 September 2014

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
56
Total Downloads

Downloads (Last 12 months)11
Downloads (Last 6 weeks)2

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Abstract

References

Index Terms

Recommendations

Nocturnal evolution of heart rate variability indices in sleep apnea

Automated scoring of obstructive sleep apnea and hypopnea events using short-term electrocardiogram recordings

An expert system for automated recognition of patients with obstructive sleep apnea using electrocardiogram recordings

Comments

Information

Published In

Publisher

Publication History

Author Tags

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

View options

PDF

eReader

Get Access

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations