A Body Area Network-Based Detection of Sleep Apnea

Sheryl LaFleur; Imad Mahgoub

9th International Conference on Body Area Networks

Research Article

A Body Area Network-Based Detection of Sleep Apnea

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/icst.bodynets.2014.258205,
    author={Sheryl LaFleur and Imad Mahgoub},
    title={A Body Area Network-Based Detection of Sleep Apnea},
    proceedings={9th International Conference on Body Area Networks},
    publisher={ICST},
    proceedings_a={BODYNETS},
    year={2014},
    month={11},
    keywords={d2 [software engineering]: miscellaneous; d26 [programming environments]: integrated environments; d32 [programming languages]: c},
    doi={10.4108/icst.bodynets.2014.258205}
}

Sheryl LaFleur
Imad Mahgoub
Year: 2014
A Body Area Network-Based Detection of Sleep Apnea
BODYNETS
ACM
DOI: 10.4108/icst.bodynets.2014.258205

Sheryl LaFleur¹^,*, Imad Mahgoub¹

1: FAU, Department of Computer & Electrical Engineering and Computer Science

*Contact email: slafleur@fau.edu

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.

Keywords: d2 [software engineering]: miscellaneous; d26 [programming environments]: integrated environments; d32 [programming languages]: c

Published: 2014-11-21
Publisher: ICST

: http://dx.doi.org/10.4108/icst.bodynets.2014.258205

A Body Area Network-Based Detection of Sleep Apnea

Abstract

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