Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System

DOI: 10.4236/eng.2013.510B054   PDF   HTML     2,579 Downloads   3,886 Views   Citations


This paper presents an efficient and easy implemented method for detecting minute based analysis of sleep apnea. The nasal, chest and abdominal based respiratory signals extracted from polysomnography recordings are obtained from PhysioNet apnea-ECG database. Wavelet transforms are applied on the 1-minute and 3-minute length recordings. According to the preliminary tests, the variances of 10th and 11th detail components can be used as discriminative features for apneas. The features obtained from total 8 recordings are used for training and testing of an adaptive neuro fuzzy inference system (ANFIS). Training and testing process have been repeated by using the randomly obtained five different sequences of whole data for generalization of the ANFIS. According to results, ANFIS based classification has sufficient accuracy for apnea detection considering of each type of respiratory. However, the best result is obtained by analyzing the 3-minute length nasal based respiratory signal. In this study, classification accuracies have been obtained greater than 95.2% for each of the five sequences of entire data.

Share and Cite:

Avci, C. and Bilgin, G. (2013) Sleep Apnea Detection Using Adaptive Neuro Fuzzy Inference System. Engineering, 5, 259-263. doi: 10.4236/eng.2013.510B054.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. Guilleminault, J. van den Hoed and M. Mitler, “Clinical Overview of the Sleep Apnea Syndromes,” In: A. R. Liss, Ed., Sleep Apnea Syndromes, New York, 1978, pp. 1-11.
[2] T. Young, M. Palta, J. Dempsey, et al., “The Occurrence of Sleep Disordered Breathing among Middle-Aged Adults,” The New England Journal of Medicine, Vol. 328, 1993, pp. 1230-1235.
[3] American Academy of Sleep Medicine, “Sleep Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research,” Sleep, Vol. 22, 1999, pp. 667-689.
[4] T. Young, P. E. Peppard and D. G. Gottlier, “Epidemiology of Obstructive Sleep Apnea, a Population Health Perspective,” American Journal of Respiratory and Critical Care Medicine, Vol. 165, 2002, pp. 1217-1239.
[5] M. Cabrero-Canosa, E. Hernandez-Pereira and V. Moret-Bonillo, “Intelligent Diagnosis of Sleep Apnea Syndrome,” Engineering in Medicine and Biology Mag-azine, Vol. 23, No. 2, 2004, pp. 72-81.
[6] A. H. Khandoker, J. Gubbi and M. Palaniswami, “Automated Scoring of Obstructive Sleep Apnea and Hypopnea Events Using Short-Term Electrocardiogram Recordings,” IEEE Transactions on Information Technology in Bio-medicine, Vol. 13, No. 6, 2009, pp. 1057-1067.
[7] G. B. Moody, R. G. Mark, A. Zoccola and S. Mantero, “Derivation of Respiration Signal from Multi-Lead ECGs,” Computers in Cardiology, 1985, pp. 113-116.
[8] C. O’Brien and C. Heneghan, “A comparison of Algorithms for Estimation of a Respiratory Signal from the Surface Electrocardiogram,” Computers in Biology and Medicine, Vol. 37, 2007, pp. 305-314.
[9] J. E. Mietus, C. K. Peng, P. Ch. Ivanov and A. L. Gold-berger, “Detection of Obstructive Sleep Apnea from Cardiac Interbeat Interval Time Series,” IEEE Computers in Cardiology, Vol. 27, 2000, pp. 753-756.
[10] M. O. Mendez, D. D. Ruini, O. P. Villantieri, M. Matteucci, T. Penzel, S. Cerutti and A. M. Bianchi, “Detection of sleep Apnea From Surface ECG Based on Features Extracted by an Autoregressive Model,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society—EMBC, Lyon, 2007, pp. 6105- 6108.
[11] D. Cysarz, R. Zerm, H. Betterman, M. Frühwirth, M. Moser and M. Kröz, “Comparison of Respiratory Rates Derived from Heart Rate Variability, ECG Amplitude, and Nasal/Oral Airflow Ambulatory Single-Lead ECG,” Annals of Biomedical Engineering, Vol. 36, No. 12, 2008, pp. 2085-2094.
[12] C. Avc1, S. Besli and A. Akbas, “Performance of the EDR Methods; Evaluations Using the Mean and Instantaneous Respiratory Rates,” The 5th International Conference on Bioinformatics and Biomedical Engineering, Wuhan, 10-12 May 2011.
[13] L. J. Epstein, D. Kristo and P. J. Strollo, “Clinical Guideline for the Evaluation, Management and Long-Term Care of Obstructive Sleep Apnea in Adults,” Journal of Clinical Sleep Medicine, Vol. 5, No. 3, 2009, pp. 263- 276.
[14] F. C. Yen, K. Behbehani, E. A. Lucas, J. R. Burk and J. R. Axe, “A Noninvasive Technique for Detecting Obstructive and Central Sleep Apnea,” IEEE Transaction on Biomedical Engineering, Vol. 44, No. 12, 1997, pp. 1262- 1268.
[15] American Sleep Disorders Association Task Force, “The Chicago Criteria for Measurements, Definitions, and Severity of Sleep Related Breathing Disorders in Adults,” Associated Professional Sleep Societies Conference, New Orleans, 1998.
[16] M. E. Tagluk and N. Sezgin, “Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets + Neural Networks,” Journal of Medical Systems, Vol. 34, No. 6, 2010.
[17] J. R. Stradling, “New Approaches to Monitoring Sleep-Related Breathing Disorders,” Sleep, Vol. 18, No. 3, 1996, pp. 77-84.
[18] P. Várady, T. Micsik, S. Benedek and Z. Benyó, “A Novel Method for the Detection of Apnea and Hipopnea Events in Respiration Signals” Transactions on Biomedical Engineering, Vol. 49, No. 9, 2002, pp. 936-942.
[19] J. Y. Tian and J. Q. Liu “Apnea Detection Based on Time Delay Neural Network,” The 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Shanghai, September 2005, pp. 2571-2574.
[20] L. S. Correa, E. Laciar, V. Mut, A. Torres and R. Jane, “Sleep Apnea Detection Based on Spectral Analysis of Three ECG—Derived Respiratory Signals,” Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3-6 September 2009, pp. 4723- 4726.
[21] C. Avc1 and A. Akbas, “Comparison of the ANN Based Classification accuracy for Real Time Sleep Apnea Detection Methods,” The 9th International Conference on Biomedical Engineering (BIOMED 2012), Innsbruck, 15- 17 February 2012.
[22] A. L. Goldberger, L. A. N. Amaral, L. Glass, J. M. Hausdorff, P. Ch. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng and H. E. Stanley, “PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for ComplexPhysiologic Signals,” Circulation, Vol. 101, No. 23, 2000, pp. e215-e220.
[23] W. R. Ruehland, P. D. Rochford, F. J. O’Donoghue, R. J. Pierce, P. Singh and A. T. Thornton, “The New AASM Criteria for Scoring Hypopneas: Impact on the Apnea Hy- popnea Index,” Sleep, Vol. 32, No. 2, 2009, pp. 150-157.
[24] J. Boyle, N. Bidargaddi, A. Sarela and M. Karunanithi, “Automatic Detection of Respiration Rate from Ambulatory Singlelead ECG,” IEEE Transactions on Information Technology in Biomedicine, Vol. 13, No. 6, 2009, pp. 890-896.
[25] Y. Chan, “Wavelet Basics,” Kluwer Academic Publishers, 1995.
[26] F. W. David, “An Introduction to Wavelet Analysis,” Birkhauser, 2002.
[27] M. Misiti, Y. Misiti, G. Oppenheim and J. M. Poggi, “Wavelet Toolbox for Use with Matlab,” User’s Guide, Version 3, 2004.
[28] F. Ebrahimi, M. Mikaeili, E. Estrada and H. Nazeran, “Automatic Sleep Stage Classification Based on EEG Signals by Using Neural Networks and Wavelet Packet Coefficients,”30th Annual International IEEE Conference, Vancouver, 2008, pp. 1151-1154.
[29] I. Daubechies, “Ten Lectures on Wavelets (CBMS-NSF Regional Conference Series in Applied Mathematics,” Society for Industrial & Applied Mathematics, Vol. 61, 1992.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.