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Sleep apnea detection using wavelet analysis of ECG derived respiratory signal

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Main Authors: Avci, Cafer, Delibasoglu, İbrahim, Akbas, Ahmet
Other Authors: cavci@acm.org
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Sleep apnea
Electrocardiography (ECG) derived respiratory (EDR)
Electrocardiography (ECG)
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/21352
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spelling my.unimap-213522012-10-15T01:02:26Z Sleep apnea detection using wavelet analysis of ECG derived respiratory signal Avci, Cafer Delibasoglu, İbrahim Akbas, Ahmet cavci@acm.org idelibasoglu@yalova.edu.tr ahmedakbas@gmail.com Sleep apnea Electrocardiography (ECG) derived respiratory (EDR) Electrocardiography (ECG) Link to publisher's homepage at http://ieeexplore.ieee.org/ The purpose of this study is to find a reliable and practical way for detecting the minute by minute occurrence of sleep apnea. For this aim, the time series of instantaneous respiratory rates (IRR) estimated with electrocardiography (ECG) derived respiratory (EDR) signals are analyzed by using wavelet decompositions. EDR signals are derived from the two sets of single-lead ECGs by 0.2-0.8 Hz band-pass filter implementations. ECG signals are obtained from apnea-ECG database on PhysioNet databank. Wavelet decompositions are implemented to the segments of 3-minutes length time series of IRRs in which the 2nd minute is accepted as deciding minute for apnea. According to the results obtained from wavelet analysis of the first data set consisting of 35 recordings, variances of 3rd, 4th, 5th and 6th detail components can be used as discriminative features which demonstrate the minute based real time apneas. The second data set consisting of 35 recordings is used for testing this consequence. For this aim, the feature vectors derived from the first data set is used for training a nonlinear auto-regressive (NARX) type artificial neural network (ANN) classifier. Due to the minute-based assessments of overnight sleep ECGs revealed that implementation of the NARX based classification following the wavelet decompositions has success level of %82.7 and %78.3 for the first (learning) and second (test) dataset, respectively. However, due to the subject-based assessment the success level is greater than %93,33. 2012-10-15T01:02:26Z 2012-10-15T01:02:26Z 2012-02-27 Working Paper p. 272-275 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6179019 http://hdl.handle.net/123456789/21352 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Sleep apnea
Electrocardiography (ECG) derived respiratory (EDR)
Electrocardiography (ECG)
spellingShingle Sleep apnea
Electrocardiography (ECG) derived respiratory (EDR)
Electrocardiography (ECG)
Avci, Cafer
Delibasoglu, İbrahim
Akbas, Ahmet
Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 cavci@acm.org
author_facet cavci@acm.org
Avci, Cafer
Delibasoglu, İbrahim
Akbas, Ahmet
format Working Paper
author Avci, Cafer
Delibasoglu, İbrahim
Akbas, Ahmet
author_sort Avci, Cafer
title Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
title_short Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
title_full Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
title_fullStr Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
title_full_unstemmed Sleep apnea detection using wavelet analysis of ECG derived respiratory signal
title_sort sleep apnea detection using wavelet analysis of ecg derived respiratory signal
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/21352
_version_ 1643793357612253184
score 13.214268

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