Wavelet analysis of heart rate variability: Impact of wavelet selection

Alexander Tzabazis*, Andreas Eisenried, David C. Yeomans, Moore IV Hyatt

*Corresponding author for this work
    7 Citations (Scopus)


    Background Wavelet transform based analysis of heart rate variability is increasingly being used for a wide variety of clinical applications. There is no gold standard as to which wavelet to use and the correlation between results obtained by using different wavelets is unknown. Methods Heart rate variability in electrocardiograms from healthy volunteers was analyzed using the following wavelets (maximum overlap discrete wavelet packet transform): Haar, Daubechies 2, 4, and 8, least asymmetric Daubechies 4 and 8, and best localized Daubechies 7 using the RHRV package in R. Correlation of power in the different frequency bands (ultra low frequency (ULF), very low frequency (VLF), low frequency (LF), high frequency (HF)) as well as total power and LF:HF ratio were calculated. Bland-Altman comparisons were also made for selected wavelets to test for agreement. Findings Correlations between results obtained by different wavelets were all statistically significant. Most correlation coefficients were moderate (0.3 ≤ r ≤ 0.7). They were, however, generally lower for the LF:HF ratio, which is commonly used to assess balance of the autonomic nervous system. Conclusion It is necessary to report which wavelet is used when performing wavelet transform based heart rate variability analysis and depending on whether one is interested in detecting onset or intensity of changes performance of wavelets will vary.

    Original languageEnglish
    JournalBiomedical Signal Processing and Control
    Pages (from-to)220-225
    Number of pages6
    Publication statusPublished - 02.2018


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