Short-time Fourier transform analysis of the phonocardiogram signal

Djebbari, Abdelghani; Reguig, F. Bereksi

doi:10.1109/icecs.2000.913008

Cited by 42 publications

(22 citation statements)

References 1 publication

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“…Where f ausc denotes the nineteenth input and is related to the auscultation focus source, mentioned before. sig is the sigmoid function in (20) with slope parameter a s .…”

Section: Initializationmentioning

confidence: 99%

Detection of heart murmurs based on radial wavelet neural network with Kalman learning

et al. 2015

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“…Where f ausc denotes the nineteenth input and is related to the auscultation focus source, mentioned before. sig is the sigmoid function in (20) with slope parameter a s .…”

Section: Initializationmentioning

confidence: 99%

Detection of heart murmurs based on radial wavelet neural network with Kalman learning

et al. 2015

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“…The details of the S1/S2 detection algorithm are illustrated in [1]. Having determined the time intervals of the S1 and S2 signals, the signals are transformed from the time to the frequency domain [7], [8]. The energy of the S1 and S2 signals is essentially concentrated around frequencies below 300 Hz.…”

Section: Biometric Verification Based On Pcg Signalsmentioning

confidence: 99%

A multiband approach to human identity verification based on PhonoCardioGram signal analysis

Beritelli

2008

2008 Biometrics Symposium

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New physiological or behavioural characteristics have to be considered in order to improve the performance of automatic mono/multi-biometric recognition systems. It has recently been shown in [1], by means of a preliminary study, that PhonoCardioGram (PCG) signals have specific individual characteristics that can be taken into consideration as a physiological sign used in a biometric system. This paper presents new results in biometric identification/verification via frequency analysis of cardiac sounds. More specifically, the paper proposes a multiband approach to PCG signals that has made it possible to enhance the linear separability and obtain an Equal Error Rate (EER) of 9 %.

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“…14,21,44 This approach is capable of distinguishing the frequency components of both S1 and S2 in PCG records. 17 However, this TFR is suitable only for signals with a slow time-varying spectrum, and suffers from the t-f resolution compromise. 43 In this regard, the quadratic energy TFD might be more adequate for a TFR that requires high time and frequency resolution, but it suffers from the presence of cross-terms: artifacts which lead to fictitious concentrations of power densities, and can hide some features of interest in the signal.…”

Section: Introductionmentioning

confidence: 99%

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

et al. 2009

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This work discusses a method for the selection of dynamic features, based on the calculation of the spectral power through time applied to the detection of systolic murmurs from phonocardiographic recordings. To investigate the dynamic properties of the spectral power during murmurs, several quadratic energy distributions have been studied, namely Wigner-Ville, Choi-Williams, smoothed pseudo Wigner-Ville, exponential, and hyperbolic T-distribution. The classification performance has been compared with that using a Short Time Fourier Transform and Continuous Wavelet Transform representations. Furthermore, this work discusses a variety of nonparametric techniques to estimate the spectral power contours as dynamic features that characterize the heart sounds (HS): instantaneous energy, eigenvectors, instantaneous frequency, equivalent bandwidth, subband spectral centroids, and Mel cepstral coefficients. In this way, the aforementioned time-frequency representations and their dynamic features were evaluated by means of their ability to detect the presence of murmurs using a simple k-Nearest Neighbors classifier. Moreover, the relevancies of the proposed dynamic features have been evaluated using a time-varying principal component analysis. The work presented is carried out using a database containing 22 phonocardiographic recordings (16 normal and 6 records with murmurs), segmented to extract 402 representative individual beats (201 per class). The results suggest that the smoothing given by the quadratic energy distribution significantly improves the classification performance for the detection of murmurs in HS. Moreover, it is shown that the power dynamic features which give the best overall classification performance are the MFCC contours. As a result, the proposed method can be implemented as a simple diagnostic tool for primary health-care purposes with high accuracy (up to 98%) discriminating between normal and pathologic beats.

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Short-time Fourier transform analysis of the phonocardiogram signal

Cited by 42 publications

References 1 publication

Detection of heart murmurs based on radial wavelet neural network with Kalman learning

Detection of heart murmurs based on radial wavelet neural network with Kalman learning

A multiband approach to human identity verification based on PhonoCardioGram signal analysis

Selection of Dynamic Features Based on Time–Frequency Representations for Heart Murmur Detection from Phonocardiographic Signals

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