|
 |
|
|||||||
|
|||||||||
Copyright ©ERS Journals Ltd 2003
Predicting sleep apnoea syndrome from heart period: a time-frequency wavelet analysis
1 Physiology Laboratory, PPEH Group & Association SYNAPSE, and 3 Pneumology Dept and Sleep Laboratory, CHU Nord, Faculté de Médecine Jacques Lisfranc, Université Jean Monnet, Saint-Etienne, France. 2 Sleep Laboratory, Department of Psychiatry, University Hospital, Geneva, Switzerland
CORRESPONDENCE: F. Roche, Laboratoire de Physiologie, Groupe PPEH & Association de Recherche SYNAPSE, Exploration Fonctionnelle Cardio-Respiratoire, CHU Nord - Niveau 6, F-42055, Saint-Etienne Cedex 2, France. Fax: 33 477828447. E-mail: Frederic.Roche@univ-st-etienne.fr
Keywords: autonomous nervous system activity, electrocardiogram holter monitoring, heart rate variability, obstructive sleep apnoea syndrome
Received: November 17, 2002
Accepted July 1, 2003
Heart rate fluctuations are a typical finding during obstructive sleep apnoea, characterised by bradycardia during the apnoeic phase and tachycardia at the restoration of ventilation. In this study, a time-frequency domain analysis of the nocturnal heart rate variability (HRV) was evaluated as the single diagnostic marker for obstructive sleep apnoea syndrome (OSAS).
The predictive accuracy of time-frequency HRV variables (wavelet (Wv) decomposition parameters from level 2 (Wv2) to level 256 (Wv256)) obtained from nocturnal electrocardiogram Holter monitoring were analysed in 147 consecutive patients aged 53.8±11.2 yrs referred for possible OSAS.
OSAS was diagnosed in 66 patients (44.9%) according to an apnoea/hypopnoea index
Time-frequency parameters calculated using wavelet transform and extracted from the nocturnal heart period analysis appeared as powerful tools for obstructive sleep apnoea syndrome diagnosis.
10. Using receiver-operating characteristic curves analysis, the most powerful predictor variable was Wv32 (W 0.758, p<0.0001), followed by Wv16 (W 0.729, p<0.0001) and Wv64 (W 0.700, p<0.0001). Classification and Regression Trees methodology generated a decision tree for OSAS prediction including all levels of Wv coefficients, from Wv2 to Wv256 with a sensitivity reaching 92.4% and a specificity of 90.1% (percentage of agreement 91.2%) with this nonparametric analysis.
This article has been cited by other articles:
|
|
 |
|
  L. T Mainardi On the quantification of heart rate variability spectral parameters using time-frequency and time-varying methods Phil Trans R Soc A, January 28, 2009; 367(1887): 255 - 275. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. de Chazal, C. Heneghan, and W. T McNicholas Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals Phil Trans R Soc A, January 28, 2009; 367(1887): 369 - 389. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Szyszko, C. Franceschini, and J. Gonzalez-Zuelgaray Reliability of a Holter-based methodology for evaluation of sleep apnoea syndrome Europace, January 1, 2009; 11(1): 94 - 99. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 F. Roche, S. Celle, V. Pichot, J-C. Barthelemy, and E. Sforza Analysis of the interbeat interval increment to detect obstructive sleep apnoea/hypopnoea Eur. Respir. J., June 1, 2007; 29(6): 1206 - 1211. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
T. Penzel Is heart rate variability the simple solution to diagnose sleep apnoea? Eur. Respir. J., December 1, 2003; 22(6): 870 - 971. [Full Text] [PDF]
|
|
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |