Heart sound analysis for symptom detection and computer-aided diagnosis

Reed, T.R.; Reed, Nancy E.; Fritzson, Peter

doi:10.1016/j.simpat.2003.11.005

Cited by 165 publications

(79 citation statements)

References 11 publications

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“…Some systolic murmurs have proven to be clinically innocent, while all the diastolic and continuous murmurs are classified as abnormal. 6,[13][14][15] The KF is proposed as a preprocessed signal to evaluate system performance based on mel-frequency cepstral coefficients and Gaussian mixture model classifiers. Accessing a heart-murmur analysis system is complicated in terms of its effect on the preprocessing stage, feature extraction, and classification of the heart.…”

Section: Resultsmentioning

confidence: 99%

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Acoustic cardiac signals analysis: a Kalman filter–based approach

Salleh¹,

Hussain²,

Swee³

et al. 2012

IJN

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Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss-Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.

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Section: Resultsmentioning

confidence: 99%

“…To quantify the estimation performance, we used SNR for heart sound, defined as follows SNR 10 log = (13) where σ N 2 and σ S 2 are the noise and signal power estimated using averages over trial as…”

Section: Effect Of Varying Noise Variancementioning

confidence: 99%

Acoustic cardiac signals analysis: a Kalman filter–based approach

Salleh¹,

Hussain²,

Swee³

et al. 2012

IJN

View full text Add to dashboard Cite

show abstract

“…The resulting outcome of this drawback is a non-unique, low-fidelity image, which changes depending on the frequency resolution (26,27). Additionally, heart sounds are nonlinear, non-sinusoidal and exponential signals and signal processing research (28) has demonstrated that the Fourier transform is not a mathematically appropriate method to study such signals.…”

Section: Discussionmentioning

confidence: 99%

Phonocardiography-based mitral valve prolapse detection using an artificial neural network

Bogdanović¹,

Ivan²,

Gavrovska³

et al. 2013

Ser J Exp Clin Res

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“…S1 contains a series of low frequency vibrations, and it is usually the longest and loudest heart sound; S2 typically has higher frequency components than S1, and its duration is shorter. In adults a third sound (S3) characterized by low frequency may be heard in correspondence with the beginning of the diastole, during the rapid filling of the ventricles and also a fourth heart sound (S4) in correspondence with the end of the diastole, during atrial contraction (Reed et al, 2004). In FPCG recordings, S3 and S4 sounds are practically undetectable (Mittra et al, 2008) and the power spectral densities and relative intensities of S1 and S2 are a function of foetal gestation age (Nagal, 1986).…”

Section: Processing Of the Fpcg Signalmentioning

confidence: 99%