Fast detection of venous air embolism in Doppler heart sound using the wavelet transform

Chan, B.C.B.; Chan, F.H.Y.; Lam, F.K.; Lui, Ping-Wing; Poon, P.W.F.

doi:10.1109/10.563293

Cited by 45 publications

(38 citation statements)

References 10 publications

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“…The use of wavelet transformations is advantageous for the analysis of transient signals such as those in phonocardiograms (CHAN et al, 1977;BENTLEY and Mc DONNEL, 1994;EL-ASIR et al, 1996;KHADRA et al, 1991;LIANa and HARTIMO, 1998).…”

Section: Discussionmentioning

confidence: 98%

Detection of the third heart sound using a tailored wavelet approach

Hult

Fjallbrant

Wranne

et al. 2004

Med. Biol. Eng. Comput.

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The third heart sound is normally heard during auscultation of younger individuals but disappears with increasing age. However, this sound can appear in patients with heart failure and is thus of potential diagnostic use in these patients. Auscultation of the heart involves a high degree of subjectivity. Furthermore, the third heart sound has low amplitude and a low-frequency content compared with the first and second heart sounds, which makes it difficult for the human ear to detect this sound. It is our belief that it would be of great help to the physician to receive computer-based support through an intelligent stethoscope, to determine whether a third heart sound is present or not. A precise, accurate and low-cost instrument of this kind would potentially provide objective means for the detection of early heart failure, and could even be used in primary health care. In the first step, phonocardiograms from ten children, all known to have a third heart sound, were analysed, to provide knowledge about the sound features without interference from pathological sounds. Using this knowledge, a tailored wavelet analysis procedure was developed to identify the third heart sound automatically, a technique that was shown to be superior to Fourier transform techniques. In the second step, the method was applied to phonocardiograms from heart patients known to have heart failure. The features of the third heart sound in children and of that in patients were shown to be similar. This resulted in a method for the automatic detection of third heart sounds. The method was able to detect third heart sounds effectively (90%), with a low false detection rate (3.7%), which supports its clinical use. The detection rate was almost equal in both the children and patient groups. The method is therefore capable of detecting, not only distinct and clearly visible/audible third heart sounds found in children, but also third heart sounds in phonocardiograms from patients suffering from heart failure.

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Section: Discussionmentioning

confidence: 98%

Detection of the third heart sound using a tailored wavelet approach

Hult

Fjallbrant

Wranne

et al. 2004

Med. Biol. Eng. Comput.

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“…DHS is obtained by simply placing the equipment of Doppler ultrasonic flow transducer over the chest of the patient [9]. An instance of DHS signal is illustrated in Fig.…”

Section: Dhs Signalsmentioning

confidence: 99%

“…The paper uses study [6] as a preprocessing and feature extraction steps as in [7] and our previous study [8]. Alternative studies have been applied to Doppler signal research area including [9] and [10]; however these studies are not satisfactory completely.…”

Section: Introductionmentioning

confidence: 98%

“…To detect these changes, physicians must give more and constant attention on the DHS. Second one is quantitative information about air volume which can not be surveyed from the DHS mentioned at [9]. In our previous study, a useful decision support system consists of Wavelet decomposition, Shorttime Fourier transform, Wavelet entropy for feature extraction and LS-SVM classifier was designed.…”

Section: Introductionmentioning

confidence: 99%

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A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

Çomak

Arslan

2010

J Med Syst

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Classification success of Support Vector Machine (SVM) depends on the characteristic of given data set and some training parameters (C and σ). In literature, a few studies have been presented for regularization of these parameters which affects classification performance directly. This study proposes a new approach based on Renyi's entropy and Logistic regression methods for parameter regularization. Our regularization procedure runs at two steps. In the first step, optimal value of kernel parameter interval is found via Renyi's entropy method and optimal C value is found via logistic regression using exponential function in the next step. In addition to, this new decision support system is applied to biomedical research area via an application related to Doppler Heart Sounds (DHS). Experimental results show the efficiency of developed regularization procedure.

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“…Significant research has been done into automated detectors for bubbles under these conditions 2,8,9,25,29 and a number of commercial systems exist. 4,6,14,15,20,28,31 However, these detection systems are primarily designed to detect large, infrequent emboli as an early warning, rather than to produce a quantitative count.…”

Section: Introductionmentioning

confidence: 99%

A Method for the Automated Detection of Venous Gas Bubbles in Humans Using Empirical Mode Decomposition

Chappell

Payne

2005

Ann Biomed Eng

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Doppler ultrasound signals are widely used to grade the quantity of circulating venous bubbles in divers. Current techniques rely on trained observers, making the grading process both time-consuming and subjective. The automated detection of bubbles, however, is confounded by the presence of other signals, primarily those arising from blood motion. Empirical Mode Decomposition was used here to calculate the intrinsic mode functions (IMFs) of a number of Doppler ultrasound signals from recreational divers, post-decompression. The IMFs provide a basis set for signal decomposition, each IMF corresponding to a different timescale in the signal. Each signal was found to comprise approximately 20 IMFs: the precise number being dependent upon the nature of the signal. A method is presented to detect bubbles using the IMF; features are first identified in the individual heart cycles, these having been previously determined using a robust peak detection method, by examining deviations from the ensemble averaged IMF. Bubbles are then identified as features appearing in more than one IMF, with significant energy in the original signal. This method has been applied to a subset of the available database and appears to perform with good sensitivity even when the signal has variable signal strength.

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Fast detection of venous air embolism in Doppler heart sound using the wavelet transform

Cited by 45 publications

References 10 publications

Detection of the third heart sound using a tailored wavelet approach

Detection of the third heart sound using a tailored wavelet approach

A Biomedical Decision Support System Using LS-SVM Classifier with an Efficient and New Parameter Regularization Procedure for Diagnosis of Heart Valve Diseases

A Method for the Automated Detection of Venous Gas Bubbles in Humans Using Empirical Mode Decomposition

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