Abstract
Polysomnographic (sleep) signals are recorded from patients exhibiting symptoms of a suspected sleep disorder such as Obstructive Sleep Apnoea (OSA). These non-stationary signals are characterised by having both quantitative information in the frequency domain and rich, dynamic data in the time domain. The collected data is subsequently analysed by skilled visual evaluation to determine whether arousals are present, an approach which is both time-consuming and subjective. This paper presents a wavelet-based methodology which seeks to alleviate some of the problems of the above method by providing: (1) an automated mechanism by which the appropriate stage of sleep for disorder observation may be extracted from the composite electroencephalograph (EEG) data set and (2) an ensuing technique to assist in the diagnosis of full arousal by correlation of wavelet-extracted information from a number of specific patient data sources (e.g. pulse oximetry, electromyogram [EMG] etc)
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Foresman, B.H., “Sleep and breathing disorders: the genesis of obstructive sleep apnoea” Journal of the American Osteopath Association, Volume 100, part 8, pp. 1–8, 2000.
Drinnan M.J., Murray A., Griffiths C.J., Gibson G.J. “Interobserver Variability in Recognising Arousal I Respiratory Sleep Disorders”. American Journal OF Critical Care Medicine Volume 158 pp358–362, 1998.
Rechtschaffen, A., Kales, A.,“A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects”. US Department of Health, Education and Welfare 1968.
Loadsman J.K., Lectures on Anaesthesia and Sleep Apnoea Department of Anaesthetics, Royal Prince Alfred Hospital, Camperdown NSW, Australia, 2001
Coleman, J., “Sleep Studies: Current Techniques and Future Trends” Journal of the Otolaryngologic Clinics Of North America, Volume 32, Number 2, pp 195–210, 1999
Engleman H.M., Martin S.E, Deary I.J., Douglas N.J. “Effect of CPAP therapy on daytime function in patients with mild sleep apnoea/hypopnoea syndrome”. Thorax; Volume52: pp 114–119, 1997.
Liam C.K., “A portal recording system for the assessment of of patients with sleep apnoea syndrome” Medical Journal of Malaysia Volume 51, part 1, pp82–88, 1996
Pachero O.R., Vaz F., “Integrated Systems for Analysis and Automatic Classification of Sleep EEG” Proceedings of the 24th Annual IEEE Conference in Bioengineering pp15–17 1998
Marsalek K., Rozman J., “Automatic Time and Frequency Domain Detection in Biomedical Signals” Proceedings of the 9th International Czech-Slovak Conference RadioElektronica pp152–5 1999.
De Gennaro L., Ferrara M., Bertini M., “Topographical Distribution of Spindles: Variations Between and Within NREM Sleep Cycles” Sleep Research Online Volume 3 Part 4: pp155–160, 2000
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
MacCallum, M., Almaini, A.E.A. (2001). The Application of the Wavelet Transform to Polysomnographic Signals. In: Tang, Y.Y., Yuen, P.C., Li, Ch., Wickerhauser, V. (eds) Wavelet Analysis and Its Applications. WAA 2001. Lecture Notes in Computer Science, vol 2251. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45333-4_35
Download citation
DOI: https://doi.org/10.1007/3-540-45333-4_35
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43034-6
Online ISBN: 978-3-540-45333-8
eBook Packages: Springer Book Archive