9th International Conference on Body Area Networks

Research Article

A Body Area Network-Based Detection of Sleep Apnea

  • @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.