Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
Identification of Obstructive Sleep Apnea from Normal Subjects: FFT Approaches Wavelets
Abdulnasir Hossen
Pages - 22 - 33     |    Revised - 30-04-2010     |    Published - 10-06-2010
Volume - 4   Issue - 2    |    Publication Date - May 2010  Table of Contents
Obstructive Sleep Apnea, Identification, Frequency Domain Analysis, FFT, Wavelets
An FFT-based algorithm for Obstructive Sleep Apnea (OSA) patient identification using R-R interval (RRI) data is investigated. The identification is done on the whole record as OSA patient or non-patient (normal). The power spectral density of the three main bands of the RRI data is computed and then three identification factors are calculated from their ratios. The first identification factor is the ratio of the PSD of the low-frequency (LF) band to that of the high-frequency (HF) band. The second identification factor is the ratio of the PSD of the very low-frequency (VLF) band to that of the low-frequency (LF) band, while the third identification factor is the ratio of the PSD of the very low-frequency (VF) band to that of the high-frequency (HF) band. The RRI data used in this work were drawn from MIT database. The three identification factors are tested on MIT trial data. The best factor, which is the (VLF/LF) PSD ratio, is then tested on MIT test data and to evaluate the performance of the identification. The efficiency of identification approaches 87%. The method is then improved by applying FFT on short records and averaging the results to get an efficiency of 93%. Another improvement was done by zero-padding the short records to double its size and then averaging the results to get an efficiency of 93%. This efficiency is compared with a previous work on the same purpose using wavelets which resulted in 90% accuracy.
CITED BY (1)  
1 Alir, S. Q., Jeoti, V., & Belhaouari, S. B. (2014, January). Wavelet Packet Based Diagnosis of Sleep Apnea using ECG Data. In Proceedings of the International Conference on Bioinformatics & Computational Biology (BIOCOMP) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
1 Google Scholar 
2 ScientificCommons 
3 Academic Index 
4 CiteSeerX 
5 refSeek 
6 iSEEK 
7 Socol@r  
8 ResearchGATE 
9 Libsearch 
10 Bielefeld Academic Search Engine (BASE) 
11 Scribd 
12 WorldCat 
13 SlideShare 
15 PdfSR 
16 Free-Books-Online 
1 AASM Task Force Report, Sleep-related breathing disorders in adults, recommendations for syndrome definition and measurement techniques in clinical research. Sleep, Vol. 22(5), 667-689, 1999.
2 Kentucky Sleep Society, http://www. kyss.org.
3 T. Young, PE. Peppard, DJ. Gottlieb, “Epidemiology of obstructive sleep apnea”, American Journal of Respiratory and Critical Care Medicine, 165: 1217-1239, 2000.
4 B. Taha, J. Dempsey, S. Weber, M. Badr, J. Skatrud, T. Young, A. Jacques, K. Seow, “Automated detection and classification of sleep-disordered breathing from conventional polysomnography data”, Sleep, 20 (11): 991-1001, 1997.
5 Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, Heart rate variability, standards of measurements, physiological interpretation, and clinical use, Circulation 93, 1043-1065, 1996.
6 P. Ranta-aho, “Tool for bio-signal analysis – Application to multi-channel single trial estimation of evoked potentials”, Master’s Thesis, University of Kuopio, 2003.
7 M. Khoo, V. Belozeroff, R. Berry, C. Sassoon, “Cardiac Autonomic Control in Obstructive Sleep Apnea”, American Journal of Respiratoty and Critical Care Medicine, 164, 807- 812, 2001.
8 I. Korhonen, “Methods for the analysis of short-term variability of heart rate and blood pressure in frequency domain”, Ph.D. Thesis, Tampere University of Technology, 1997.
9 J. Mietus, C. Peng, P. Ivanov, A. Goldberger, “Detection of obstructive sleep apnea from cardiac interbeat interval time series”, Comp. Cardiology, 27, 753-756, 2000.
10 A. Hossen, "The FFT as an identification tool for obstructive sleep Apnea", WASET, Bangkok, December 2009.
11 http://www.physionet.org/physiobank/database/apnea-ecg/.
12 http://www.physionet.org/physiobank/database/apnea-ecg/.
13 http://www.physionet.org/physiotools /ecgpuwave.
14 http://www.physionet.org/physiotools /ecgpuwave.
15 B. Jr. Thomas, “Overview of RR variability”, heart rhythm instruments Inc., 2002.
16 R. M. Rangayyan, “Biomedical Signal Analysis: A Case-Study Approach”, IEEE Press, 466-472, 2001.
17 A. Hossen, “A soft-decision algorithm for obstructive patient classification based on fast estimation of wavelet entropy of RRI data”, Technology and Health Care, 13, 151-165.
Associate Professor Abdulnasir Hossen
Sultan Qaboos University - Oman