Abstract
Recordings of biological signals such as vibrocardiography often contain contaminating noise. Noise sources may include respiratory, gastrointestinal, and muscles movement, or environmental noise. Depending on individual physiology and sensor location, the vibrocardiographic (VCG) signals may be obscured by these noises in the time-frequency plane, which may interfere with automated characterization of VCG. In this study, polynomial chirplet transform (PCT) and smoothed pseudo Wigner-Ville distribution (SPWVD) were used to estimate the instantaneous frequency (IF) of two simulated VCG signals. One simulated signal contained a time-varying IF while the other had a fixed IF. The error in estimating IF was then calculated for signal-to-noise ratios (SNR) from −10 to 10 dB. Analysis was repeated 100 times at each level of noise using randomized sets of white noise. Error analysis showed that the range of errors in estimating IF was wider when SNR decreased. Results also showed that PCT tended to outperform SPWVD at high SNR. For example, PCT was more accurate at SNR > 3 dB for a simulated VCG signal with constant frequency components, at SNR>−10 dB for a simulated VCG signal with time-varying frequency, and at SNR > 0 for an actual VCG.
Highlights
Vibrocardiographic signals (VCG) are the vibrations induced by cardiac activity and measured at the chest surface
The current study aims at studying the effects of noise on the performance of two Time-frequency analysis (TFA) methods that may be used for VCG analysis: polynomial chirplet transform (PCT) and smoothed pseudo Wigner-Ville distribution (SPWVD)
The goal of this study was to compare the ability of the polynomial chirplet transform and smoothed pseudo Wigner-Ville distribution in providing accurate time-frequency estimates for VCG signals contaminated by white noise
Summary
Vibrocardiographic signals (VCG) are the vibrations induced by cardiac activity and measured at the chest surface. Various methods have been utilized to remove noise from electrocardiographic and phonocardiography signals [20,21,22,23,24]. To the best of authors’ knowledge, there are no studies that focused on the analysis of VCG in the presence of noise. Noise removal is important as it may interfere with the operation of signal analysis methods. The current study aims at studying the effects of noise on the performance of two TFA methods that may be used for VCG analysis: polynomial chirplet transform (PCT) and smoothed pseudo Wigner-Ville distribution (SPWVD). Results are presented and discussed, followed by conclusions in the last section
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