Localization of Heart Sounds Based on S-Transform and Radial Basis Function Neural Network

Moukadem, Ali; Dieterlen, Alain; Hueber, Nicolas; Brandt, Christian

doi:10.1007/978-3-642-21683-1_42

Cited by 13 publications

(10 citation statements)

References 11 publications

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“…It uses the S-matrix like the SRBF method (0-100 Hz) [6] and it calculates the Shannon Energy (SE) of the local spectrum calculated by the S-transform for each sample of the signal x(t). Then, the extracted envelope is smoothed by applying an average filter (figure 1).…”

Section: Sse Localization Methodsmentioning

confidence: 99%

“…The advantage of the Shannon energy transformation is its capacity to emphasize the medium intensities and to attenuate low intensities of the signal which represents the local spectrum in the case the SSE method. The main difference between the SSE and the SRBF method [6] is the training phase needed for the RBF module. The RBF neural network in the SRBF method can be considered as a non-linear filter which is replaced with a simple average filter in the SSE method.…”

Section: Figure 1: Block Diagram Of Sse Methodsmentioning

confidence: 99%

“…This method aims at extracting the envelope of the signal by applying the features extracted from the S-Transform matrix of the heart sound signal to the radial basis function (RBF) neural network (SRBF). The SRBF method was shown to have a significant enhancement in term of sensitivity comparing to other existing methods [6].…”

Section: Introductionmentioning

confidence: 92%

“…This was not surprising because the method operates directly on the heart sound without any feature extraction step. To deal with this problem, a method for heart sounds localization named SRBF was proposed [6]. This method aims at extracting the envelope of the signal by applying the features extracted from the S-Transform matrix of the heart sound signal to the radial basis function (RBF) neural network (SRBF).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A robust heart sounds segmentation module based on S-transform

Moukadem

Dieterlen

Hueber

et al. 2013

Biomedical Signal Processing and Control

143

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This paper presents a new module for heart sounds segmentation based on S-Transform. The heart sounds segmentation process segments the PhonoCardioGram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2. An

show abstract

Section: Sse Localization Methodsmentioning

confidence: 99%

Section: Figure 1: Block Diagram Of Sse Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A robust heart sounds segmentation module based on S-transform

Moukadem

Dieterlen

Hueber

et al. 2013

Biomedical Signal Processing and Control

143

View full text Add to dashboard Cite

show abstract

“…This was not surprising because the method operates directly on the heart sound without any feature extraction step. To deal with this problem, we proposed a method for heart sounds localization named SRBF [6]. This method aims at extracting the envelope of the signal by applying the features extracted from the S-Transform matrix of the heart sound signal to the radial basis function (RBF) neural network.…”

Section: Srbf Localization Methodsmentioning

confidence: 99%