Time-frequency characterization of fetal phonocardiographic signals using wavelet scalogram
21 Nov 2011-Journal of Mechanics in Medicine and Biology (World Scientific Publishing Company)-Vol. 11, Iss: 02, pp 391-406
TL;DR: The proposed method for time-frequency analysis (TFA) and the associated pre-processing have been shown to be suitable for the characterization of fPCG signals, yielding relatively good and robust results in the experimental evaluation.
Abstract: Fetal phonocardiography is a simple and noninvasive diagnostic technique for surveillance of fetal well-being. The fetal phonocardiographic (fPCG) signals carry valuable information about the anatomical and physiological states of the fetal heart. This article is concerned with a study of continuous wavelet transform (CWT)-based scalogram in analyzing the fPCG signals. The scalogram has both spatial and frequency resolution powers, whereas traditional spectral estimation methods only have the frequency resolution ability. The fPCG signals are acquired by a specially developed data recording system. Segmentation of these signals into fundamental components of fetal heart sound (S1 & S2) is carried out through envelope detection and thresholding techniques. CWT-based scalogram is used for time-frequency characterization of the segmented fPCG signals. It has been shown that the wavelet scalogram provides enough features of the fPCG signals that will help to obtain qualitative and quantitative measurements of the time-frequency characteristics of the fPCG signals and consequently, assist in diagnosis. The proposed method for time-frequency analysis (TFA) and the associated pre-processing have been shown to be suitable for the characterization of fPCG signals, yielding relatively good and robust results in the experimental evaluation.
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References
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TL;DR: The Illustrated Wavelet Transform Handbook: Introductory Theory and Applications in Science, Engineering, Medicine and Finance as discussed by the authors is a comprehensive overview of wavelet transform applications in science, engineering, medicine and finance.
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942 citations
TL;DR: The theory of a new general class of signal energy representations depending on time and scale is developed, and specific choices allow recovery of known definitions, and provide a continuous transition from Wigner-Ville to either spectrograms or scalograms (squared modulus of the WT).
Abstract: The theory of a new general class of signal energy representations depending on time and scale is developed Time-scale analysis has been introduced recently as a powerful tool through linear representations called (continuous) wavelet transforms (WTs), a concept for which an exhaustive bilinear generalization is given Although time scale is presented as an alternative method to time frequency, strong links relating the two are emphasized, thus combining both descriptions into a unified perspective The authors provide a full characterization of the new class: the result is expressed as an affine smoothing of the Wigner-Ville distribution, on which interesting properties may be further imposed through proper choices of the smoothing function parameters Not only do specific choices allow recovery of known definitions, but they also provide, via separable smoothing, a continuous transition from Wigner-Ville to either spectrograms or scalograms (squared modulus of the WT) This property makes time-scale representations a very flexible tool for nonstationary signal analysis >
326 citations
TL;DR: It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary valve component P2, of the second sound S2 of a normal PCG signal.
Abstract: This paper presents the applications of the spectrogram, Wigner distribution and wavelet transform analysis methods to the phonocardiogram (PCG) signals. A comparison between these three methods has shown the resolution differences between them. It is found that the spectrogram short-time Fourier transform (STFT), cannot detect the four components of the first sound of the PCG signal. Also, the two components of the second sound are inaccurately detected. The Wigner distribution can provide time-frequency characteristics of the PCG signal, but with insufficient diagnostic information: the four components of the first sound, SI, are not accurately detected and the two components of the second sound, S2, seem to be one component. It is found that the wavelet transform is capable of detecting the two components, the aortic valve component A2 and pulmonary value component P2, of the second sound S2 of a normal PCG signal. These components are not detectable using the spectrogram or the Wigner distribution. Ho...
134 citations
TL;DR: The authors believe that low oscillation complex wavelets have wide applicability to other practical signal analysis problems, and their possible application to two such problems is discussed briefly—the interrogation of arrhythmic ECG signals and the detection and characterization of coherent structures in turbulent flow fields.
130 citations