Kunihiko Tanaka1, Alan R Hargens. 1. Department of Orthopaedic Surgery, 350 Dickinson Street, University of California, 8894 San Diego, CA 92103, USA.
Abstract
INTRODUCTION: Wavelet transform is used for time-frequency analysis. Recently, discrete wavelet transform (DWT) has been used to analyze R-R interval or heart rate variability. However, we hypothesized that wavelet packet transform (WPT) is a better way to analyze such variability. In the present study, we compared resolution of frequency band and amplitude, which are used for analysis of the variability, with DWT and WPT, followed by Hilbert transform. METHODS: A chirp signal which covers all frequency bands used for R-R interval variability was employed as a simulated signal. Levels 1-6 of DWT and level 3 of WPT were used for signal analysis. Amplitudes of the gained signal were evaluated with Hilbert transform. Differences in error of the gained amplitude from expected amplitude between CWT and DWT for low-frequency (LF) and high-frequency (HF) components were compared. To evaluate time-dependent changes in R-R interval variability, head-up tilt (HUT) was employed as an orthostatic challenge. RESULTS: Errors for both HF and LF, derived from the simulated signal with WPT, were significantly smaller than those of DWT. With HUT, time dependent changes in LF, HF, and LF/HF were observed. DISCUSSION: Although DWT is a valuable method for time-frequency analysis, WPT is a more appropriate method to utilize wavelet transform due to the equivalent resolution of the gained frequency band. WPT for time-frequency analysis improves analysis of time-dependent changes in R-R interval variability.
INTRODUCTION: Wavelet transform is used for time-frequency analysis. Recently, discrete wavelet transform (DWT) has been used to analyze R-R interval or heart rate variability. However, we hypothesized that wavelet packet transform (WPT) is a better way to analyze such variability. In the present study, we compared resolution of frequency band and amplitude, which are used for analysis of the variability, with DWT and WPT, followed by Hilbert transform. METHODS: A chirp signal which covers all frequency bands used for R-R interval variability was employed as a simulated signal. Levels 1-6 of DWT and level 3 of WPT were used for signal analysis. Amplitudes of the gained signal were evaluated with Hilbert transform. Differences in error of the gained amplitude from expected amplitude between CWT and DWT for low-frequency (LF) and high-frequency (HF) components were compared. To evaluate time-dependent changes in R-R interval variability, head-up tilt (HUT) was employed as an orthostatic challenge. RESULTS: Errors for both HF and LF, derived from the simulated signal with WPT, were significantly smaller than those of DWT. With HUT, time dependent changes in LF, HF, and LF/HF were observed. DISCUSSION: Although DWT is a valuable method for time-frequency analysis, WPT is a more appropriate method to utilize wavelet transform due to the equivalent resolution of the gained frequency band. WPT for time-frequency analysis improves analysis of time-dependent changes in R-R interval variability.