Analysis of seismocardiographic signals using polynomial chirplet transform and smoothed pseudo Wigner-Ville distribution

Abstract

Seismocardiographic (SCG) signals are chest surface vibrations induced by cardiac activity. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. One approach of determining signal features is to estimate its time-frequency characteristics. In this regard, four different time-frequency distribution (TFD) approaches were used including short-time Fourier transform (STFT), polynomial chirplet transform (PCT), Wigner-Ville distribution (WVD), and smoothed pseudo Wigner-Ville distribution (SPWVD). Synthetic SCG signals with known time-frequency properties were generated and used to evaluate the accuracy of the different TFDs in extracting SCG spectral characteristics. Using different TFDs, the instantaneous frequency (IF) of each synthetic signal was determined and the error (NRMSE) in estimating IF was calculated. STFT had lower NRMSE than WVD for synthetic signals considered. PCT and SPWVD were, however, more accurate IF estimators especially for the signal with time-varying frequencies. PCT and SPWVD also provided better discrimination between signal frequency components. Therefore, the results of this study suggest that PCT and SPWVD would be more reliable methods for estimating IF of SCG signals. Analysis of actual SCG signals showed that these signals had multiple spectral components with slightly time-varying frequencies. More studies are needed to investigate SCG spectral properties for healthy subjects as well as patients with different cardiac conditions.

Adverse Hemodynamic Conditions Associated with Mechanical Heart Valve Leaflet Immobility

“…CFD may also provide valuable information to speed up the design of implantable devices during the prototype development [ 29 ] and reduce the costs and risks associated with new heart valve designs [ 30 ]. Hence, analysis of flow dynamics and the resulting turbulence [ 31 , 32 , 33 , 34 ] and sounds [ 35 , 36 , 37 , 38 , 39 , 40 ] has been an active area of research.…”

Grouping similar seismocardiographic signals using respiratory information

“…The SCG signals mainly contain low-frequency waves where the human auditory sensitivity is low and cannot sufficiently extract the signal characteristics accurately [11], [12]. Hence measurement and analysis of these signals may be done using computerized data acquisition and analysis, which would provide enhanced qualitative and quantitative description of the signal characteristics in both time and frequency domains [13]- [15].…”

Recent Advances in Seismocardiography

“…Several studies were done to determine the most accurate methods for extracting time-frequency features of the SCG signals [67,104,105]. In these studies, different time-frequency distribution techniques were utilized, including short-time Fourier transform [67,104,105], polynomial chirplet transform (PCT) [67,105], wavelet transform with different mother functions [67], Wigner-Ville distribution, and smoothed pseudo Wigner-Ville distribution (SPWVD) [105]. PCT and SPWVD were found to have the most accurate time-frequency distribution estimations and appeared more suited for determining the frequency content of SCG signals.…”