@article{
 title = {Detection of obstructive sleep apnea from cardiac interbeat
interval time series},
 type = {article},
 year = {2000},
 identifiers = {[object Object]},
 pages = {753-756},
 id = {c20d088a-4e44-3c0b-ba13-ebf480889541},
 created = {2015-04-30T11:14:24.000Z},
 file_attached = {true},
 profile_id = {5e73f9af-1f10-3a70-a285-d3fbb699efe2},
 group_id = {e134fb94-8118-3ebe-b82d-bfbf0a8ae633},
 last_modified = {2015-04-30T12:11:50.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 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},
 bibtype = {article},
 author = {Mietus, J.E. and Peng, C.K. and Ivanov, P.Ch. and Goldberger, a.L.},
 journal = {Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163)}
}

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