A Method for the Analysis of Respiratory Sinus Arrhythmia Using Continuous Wavelet Transforms

A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. In studies of cardio-respiratory interaction during sleep, p...

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2008-05, Vol.55 (5), p.1640-1642
Main Authors: Cnockaert, Laurence, Migeotte, Pierre-FranÇois, Daubigny, Lise, Prisk, G. Kim, Grenez, Francis, Sa, Rui Carlos
Format: Article
Language:English
Subjects:
Algorithms
Arrhythmia, Sinus - diagnosis
Arrhythmia, Sinus - physiopathology
Cardio-respiratory interaction
Cardiology
continuous wavelet transform (CWT)
Continuous wavelet transforms
Delay effects
Diagnosis, Computer-Assisted - methods
Discrete wavelet transforms
Electrocardiography - methods
Frequency conversion
Hafnium
Heart Rate
Heart rate variability
heart rate variability (HRV)
Humans
Reproducibility of Results
Respiratory Function Tests - methods
Respiratory Mechanics
respiratory sinus arrhythmia (RSA)
Sensitivity and Specificity
Time measurement
Wavelet analysis
Wavelet transforms
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Summary:A continuous wavelet transform-based method is presented to study the nonstationary strength and phase delay of the respiratory sinus arrhythmia (RSA). The RSA is the cyclic variation of instantaneous heart rate at the breathing frequency. In studies of cardio-respiratory interaction during sleep, paced breathing or postural changes, low respiratory frequencies, and fast changes can occur. Comparison on synthetic data presented here shows that the proposed method outperforms traditional short-time Fourier-transform analysis in these conditions. On the one hand, wavelet analysis presents a sufficient frequency-resolution to handle low respiratory frequencies, for which time frames should be long in Fourier-based analysis. On the other hand, it is able to track fast variations of the signals in both amplitude and phase for which time frames should be short in Fourier-based analysis.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2008.918576