Detection of Obstructive Sleep Apnea (OSA) is important as it is closely related to the cause of sudden death, due to heart failure during sleep. In this article, the method for OSA detection utilizes electrocardiographic parameters: QRS interval, QT interval and RT interval. The parameters are used for inputs of discriminant analysis. To find the performance of the detection method, a 5-fold cross validation is conducted. The performance are presented in terms of accuracy. Using clinical data of Apnea Database form MIT-BIH, the detection method shows a good performance.

Topics
Diseases and conditions, Scientific society and organization
1.
Abidin
 
Z.
 
2012
.
Faktor Risiko Penyakit Jantung Koroner pada Pasien Rawat Inap DiCardiovascular Care Unit (Cvcu) Cardiac Centre Rsup Dr. Wahidin Sudirohusodo Makassar periode Januari-Juli 2008
.
Disertasi, Fakultas Kedokteran Universitas
Google Scholar
 
2.
Bradley
,
T. D.
, &
Floras
,
J. S.
 
2009
.
Obstructive Sleep Apnoea and its Cardiovascular Consequences
.
The Lancet
,
373
,
82
–
93
.
https://doi.org/10.1016/S0140-6736(08)61622-0
Google Scholar
Crossref
Search ADS
 
3.
Choi
,
S. H.
,
Yoon
,
H.
,
Kim
,
H. S.
,
Kim
,
H. B.
,
Kwon
,
H. B.
,
Oh
,
S. M.
,
Leeb
,
Y. J.
, &
Parkc
,
K. S.
 
2018
.
Real-Time Apnea-Hypopnea Event Detection During Sleep by Convolutional Neural Networks
.
Computers in Biology and Medicine
,
100
,
123
–
131
.
https://doi.org/10.1016/j.compbiomed.2018.06.028
Google Scholar
Crossref
Search ADS
PubMed
 
4.
Kluwer
,
W.
 
2011
.
ECG Interpretation Made Incredibly Easy
 5th edition. 
China
:
Lippicott-Williams & Wilkins
.
Google Scholar
 
5.
Johnson
,
R.A
 dan
Wichern
,
D.W.
 
2007
.
Applied Multivariate Statistical Analysis
.
USA
:
Pearson Education, Inc
.
Google Scholar
 
6.
Laiali
,
A.
,
Khaled
,
E.
, &
Miad
,
F.
 
2012
.
Obstructive Sleep Apnea Detection Using SVM-Based Classification of ECG Signal Features
.
International IEEE EMBS Conference
, vol
29
.
Google Scholar
 
7.
Omidvari
 
K.
 
2000
. Sleep Disorders. Ali Juzar, Summer Warren.
Pulmonary pathopysiology
.
New York
:
McGraw-Hill
. p.
283
–
90
Google Scholar
 
8.
Parak
,
J.
, &
Havlik
,
J.
 
2011
.
ECG Signal Processing and Heart Rate Frequency Detection Methods
.
In 19th Annual Conf. on Technical Computing
,
8
.
Google Scholar
 
9.
Sartono
,
B.
 dkk.
2003
.
Analisis Peubah Ganda
.
Institut Pertanian Bogor
,
Bogor
.
Google Scholar
 
10.
Viigimae
,
M.
,
Karai
,
D.
,
Pilt
,
K.
,
Pirn
,
P.
,
Huhtala
,
H.
,
Polo
,
O.
,
Meigas
,
K.
, &
Kaik
,
J.
 
2017
.
QT Interval Variability Index and QT Interval Duration During Different Sleep Stages in Patients with Obstructive Sleep Apnea
.
Sleep Medicine
,
37
,
160
–
167
.
https://doi.org/10.1016/j.sleep.2017.06.026
Google Scholar
Crossref
Search ADS
PubMed
 
11.
Zhu
,
W.
,
Zeng
,
N.
, &
Wang
,
N.
 
2010
.
Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS implementation
.
NESUG proceeding:health care and life sciences
, Batimore, Maryland,
1
,
1
–
9
.
Google Scholar
 
This content is only available via PDF.
© 2019 Author(s).
2019
Author(s)