Detection of obstructive sleep apnea from cardiac interbeatinterval
time series

Mietus, J.E.   Peng, C.K.   Ivanov, P.Ch.   Goldberger, A.L.  

This paper appears in: Computers in Cardiology 2000
Publication Date: 2000
On page(s): 753-756
Meeting Date: 09/24/2000 - 09/27/2000
Location: Cambridge, MA, USA
ISBN: 0-7803-6557-7
References Cited: 7
INSPEC Accession Number: 6867023
DOI: 10.1109/CIC.2000.898634
Posted online: 2002-08-06 23:25:43.0

Abstract
Presents a new automated method to diagnose and quantify obstructive
sleep apnea from single-lead electrocardiograms based on the detection
of the periodic oscillations in cardiac interbeat intervals that are
often associated with prolonged cycles of sleep apnea. This technique
employs the Hilbert transformation of the sinus interbeat interval
time series to derive the instantaneous amplitudes and frequencies of
the series and calculates their averages and standard deviations over
a moving 5-minute window. The authors then apply a thresholding
technique and detect continuous sequences of those windows that lie
within threshold limits. When applied to the Computers in Cardiology
sleep apnea test data, the authors' algorithm correctly classified 28
out of 30 cases (93.3%) of both sleep apnea and normal subjects, and
correctly identified the presence or absence of sleep apnea in 14,591
out of a total of 17,268 minutes (84.5%) of the data from the test set