Time–frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Abstract

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

Second Heart Sound (S2) Decomposition by Hilbert Vibration Decomposition (HVD) for Affective Signal Modeling and Learning

“…Several techniques like time frequency, short time Fourier transform (STFT) [1,2], Wigner-Ville distribution (WVD) [3], wavelet transform (WT) [3,4], are applied to analyze the PCG signal. All these techniques are based on the classical Fourier analysis which considers the system/function stationary.…”

Signal and Image Denoising Using Wavelet Transform

“…The frequency components of a normal PCG signals can be rise up 200Hz, and the energy of the most significant components concentrates around the frequency band 100-150Hz (Ergen, Tatar et al 2010). The frequency bands of the signal are very important when we use the denoising technique using DWT approaches.…”

“…This records of acoustic signals are unfortunately disturbed by various factors which effecting as noise. These effects decrease the performance of visual and computerized analysis (Akay, Semmlow et al 1990;Ergen, Tatar et al 2010). …”

“…Haar, Daubechies, Symlets, Coiflets, Biorthogonal and etc. ) consist in how these scaling signals and the wavelets are defined (Zou and Tewfik 1993;Blu 1998;Ergen, Tatar et al 2010). …”

Instantaneous Characteristics of Nonlinear Torsion Pendulum and Its Application in Parameter Estimation of Nonlinear System

“…To calculate the instantaneous frequency, many methods are developed such as short-time Fourier transformation [13,14], wavelet transformation [15,16], Wigner-Vile Distribution [17,18], and Hilbert transform [19]. Reference [11] presented the construction method of analytic signal based on Hilbert transform, which is used for the calculation of instantaneous frequency.…”