Detection of Sleep Apnea from Electrocardiogram Using Wavelet Transform


(*) Corresponding author


Authors' affiliations


DOI's assignment:
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)

Abstract


The objective of this study is to investigate the characteristic of electrocardiogram (ECG) for detecting sleep apnea. The heart rate variability (HRV) was extracted from RR interval information from ECG trace. In this study the HRV within 5 minutes sliding window was analyzed with continuous wavelet transform.  The sum energy of wavelet power spectrum density between 0.015 and 0.05 Hz was studied in this analysis. Then the position of sliding 5-minute window was shifted along the HRV signal in steps of 3 minutes until the end of trace. The interquartile range (IQR) of the analyzed energy was used as the maker in this investigation. As the results it is found that the IQR performs in order to detect sleep apnea with area under receiving operating characteristics of 0.83. The performance in sleep apnea detection achieved at 100% sensitivity and 70% specificity.
Copyright © 2018 Praise Worthy Prize - All rights reserved.

Keywords


Sleep Apnea; Electrocardiogram; Wavelet Transform; Heart Rate Variability; Stroke

Full Text:

PDF


References


P. C. Deegan, W. T. McNicholas, Pathophysiology of obstructive sleep apnea. Respiratory Disorders during Sleep, Vol. 3, pp.28-62, 1998.

Z. Shinar, A. Baharav, S. Akselrod, Obstructive sleep apnea detection based on electrocardiogram analysis, Computer in Cardiology, Vol. 27 pp.267-270, 2000.

S. Reischa, J. Timmerb, H. Steltnerb, K. H. Rühlec, J. H. Fickerd, J. Guttmann, Detection of obstructive sleep apnea by analysis of phase angle using the forced oscillation signal, Respiration Physiology Vol.123, pp.87-89, 2000.
T. Penzel, J. McNames, P. de Chazal, B. Raymond, A. Murray, G. Moody, Systematic comparison of different algorithms for apnea detection based on electrocardiogram recordings, Medical & Biological Engineering & Computing Vol. 40, pp. 402-407, 2002.

P. S. Addison, The illustrated wavelet transform handbook: Introductory theory and applications in science, engineering, medicine and finance, (Bristol and Philadelphia, Institute of Physics Publishing, 2002).

Apnea-ECG Database [online], available at:
http://www.physionet.org/physiobank/database/apnea-ecg/

J. N. Watson, N. Uchaipichat, P. S. Addison, G. R. Clegg, C. E. Robertson, T. Eftestol, P. A. Steen, Improved prediction of defibrillation success for out-of-hospital VF cardiac arrest using wavelet transform methods, Resuscitation, Vol. 63, Iss. 3, December 2004, Pages 269-275.

J. N. Watson, P. S. Addison, N. Uchaipichat, A. S. Shah, N. R. Grubb, Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients, Computers in Biology and Medicine, Vol. 37, Iss. 4, April 2007, Pages 517-523.

A. S. Shah, P. S. Addison, J. N. Watson, N. Uchaipichat, N. R. Grubb, Wavelet analysis using a modulus maxima method to predict rhythm status after cardioversion for persistent atrial fibrillation Heart Rhythm, Vol. 3, Iss. 5, Supplement 1, May 2006, Page S317.

N. Uchaipichat, S. Mitaim, Development of Ventricular Fibrillation Detection Using Wavelet-based Technique, (2009) International Review on Modelling and Simulations (IREMOS), 2 (1), pp. 118-123.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize