论文信息 - A Body Area Network-Based Detection of Sleep Apnea

A Body Area Network-Based Detection of Sleep Apnea

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.

Imad Mahgoub | Sheryl LaFleur | I. Mahgoub | Sheryl LaFleur

[1] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[2] G. B. Moody,et al. Heart rate and respiratory rhythm dynamics on ascent to high altitude. , 1995, British heart journal.

[3] George B. Moody,et al. Derivation of Respiratory Signals from Multi-lead ECGs , 2008 .

[4] Athanasios V. Vasilakos,et al. Proceedings of the Fifth International Conference on Body Area Networks , 2010 .

[5] Imad Mahgoub,et al. A Personal Body Area Network as a Pre-Screening Surrogate to the Polysomnography , 2013, BODYNETS.

[6] G. Moody,et al. Clinical Validation of the ECG-Derived Respiration (EDR) Technique , 2008 .

[7] Hlaing Minn,et al. Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG , 2011, IEEE Transactions on Information Technology in Biomedicine.

[8] Borje Darpo,et al. 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 , 2006, Journal of clinical pharmacology.