@article{
 title = {Detection of sleep apnea in single channel ECGs from the PhysioNet
data base},
 type = {article},
 year = {2000},
 identifiers = {[object Object]},
 pages = {263-266},
 id = {b57b2750-c641-340e-baa8-297de1e296c1},
 created = {2015-04-30T11:14:23.000Z},
 file_attached = {true},
 profile_id = {5e73f9af-1f10-3a70-a285-d3fbb699efe2},
 group_id = {e134fb94-8118-3ebe-b82d-bfbf0a8ae633},
 last_modified = {2015-04-30T12:11:46.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {The authors have analyzed single channel ECGs from the Marburg
Sleep Laboratory downloaded from Physionet. 35 Data sets with sleep
apnea annotation (Learning Set) and 35 testing cases without annotation
were available. The authors' analysis and annotations are based on
spectral components of heart rate variability. Frequency analysis was
performed using Fourier and Wavelet Transformation with appropriate
application of the Hilbert Transform. Classification is based on four
frequency bands. The authors defined: ULF band (0-0.013 Hz) VLF band
(0.013-0.0375 Hz) LF band (0.0375-0.06 Hz) and the HF band (0.17-0.28
Hz). For classification linear discriminant functions using spectral
components and other variables derived from the recorder have been used.
Classification of cases by means of three variables resulted for the
Learning Set in a sensitivity for apnea of 95.0% at a specificity of
100.0% and for the minutes annotation a sensitivity was 90.8% and
specificity 92.7% Allocation per minute based on a discriminant function
using 30 variables},
 bibtype = {article},
 author = {Schrader, M. and Zywietz, C. and Einem, V. Von and Widiger, B. and Joseph, G.},
 journal = {Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163)}
}

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