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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 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.

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.

This article has been cited by other articles:

				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]