Classification of heart sound based on s-transform and neural network

Hadi, Harisnal; Mashor, Mohd Yusoff; Suboh, Mohd Zubir; Saat, Mohamed

doi:10.1109/isspa.2010.5605543

Cited by 22 publications

(11 citation statements)

References 4 publications

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“…A method of feature extraction and classification of heart sound signals using S-Transform technique to extract the heart sound features have also been utilized [25]. A Multilayer Perceptron Network was used as heart sound classifier.…”

Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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Globally increasing rate of cardiovascular diseases represents one of the primary health care challenges and necessitates broader population screening for earlier intervention. Present cardiovascular function assessment techniques encompass a broad range of diagnostic tools which in most cases involve high and mid level cost instrumentation. They include among others instrumentation as a part of nuclear cardiology, cardiac catheterization, cardiac ultrasound, relatively basic multiple or simple channel ECG acquisition systems, and other. These techniques and related instrumentation represent very useful and powerful tools to diagnose and/or manage particular cardiac abnormalities; however they are not suitable for broader population low cost basic supplemental cardiac screening. Therefore, these studies focused on a structured assessment of cardiac sound traits as related auscultation is a part of standard medical exams and globally accepted procedures. For these reasons a dedicated cardiac sounds assessment algorithm was developed and has been tested on heart sound database that contains 565 signals encompassing 202 normal and 363 abnormal sounds. A broad range of diverse cardiac sound indicators, versatility of database, and derived characteristics validate potency of possible clinical usefulness and potential for a low cost broader diagnostic screening. In particular, the group of abnormal heart sounds contains samples of 35 different heart abnormalities which are divided into two groups including Group I that contains 28 different heart abnormalities that are 81.81% successfully detectable by the developed algorithm and Group II that contains 7 different heart abnormalities that do not exhibit the frequency change of S1 sound and are not classified by the proposed method. Normal heart sounds in 97.03% of cases were successfully classified with the developed method which is addressing the needs for a selected cardiac screening in light of increasing prevalence of cardiovascular diseases.

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Section: Methods Utilized In Heart Sounds Signal Classificationmentioning

confidence: 99%

Diagnostic spectral segregation of cardiac sounds features

Zeljković

Druzgalski

Bojic

et al. 2014

2014 Pan American Health Care Exchanges (PAHCE)

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“…It can be viewed as a frequency dependent STFT or a phase corrected wavelet transform [7]. The S-Transform has been proven in to perform better than other time-frequency /scale transforms for heart sounds signal analysis [8,9].…”

Section: Introductionmentioning

confidence: 99%

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

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“…In order to detect and identify cardiac abnormality using cardiac sound signals, various methods based on the short-time Fourier transform (STFT), wavelet transform (WT), wavelet packet decomposition and TQWT have been presented in [8,13,[23][24][25][26]. The S-transform based classification of S1 and S2 heart sounds has been presented in [27,28]. The hidden Markov models have been used for classification of cardiac sound signals in [29].…”

Section: Introductionmentioning

confidence: 99%

Detection of septal defects from cardiac sound signals using tunable-Q wavelet transform

Patidar

Pachori

Garg

2014

2014 19th International Conference on Digital Signal Processing

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In this paper, we present a new method for detection of septal defects from cardiac sound signals using tunable-Q wavelet transform (TQWT). To begin with, the cardiac sound signals have been segmented into heart beat cycles using constrained TQWT based approach. In order to extract the timefrequency domain based features, TQWT based decomposition of heart beat cycles has been performed up to sixth stage. The murmurs have more fluctuations than heart sounds. Therefore, to characterize murmurs in cardiac sound signals, proposed feature set was formed with fluctuation indices that have been computed from reconstruction of decomposed sub-bands. Then, this feature set containing twenty one features has been used to classify cardiac sound signals for detection of septal defects. In order to validate the usefulness of the proposed method for diagnosis of septal defects, besides cardiac sound signals for septal defects and normal, this study also considers signals to be detected for valvular defects and other defects like ventricular hypertrophy, constrictive pericarditis etc. The classification has been performed using least squares support vector machine (LS-SVM) with radial basis (RBF) kernel function. In order to tune the quality-factor (Q) of the TQWT to provide highest classification accuracy, the experiment has been conducted with varying value of Q. The experimental results show that the proposed method has provided significant classification performance at Q = 2 for various clinical cases as comprised in the publicly available datasets. The test results demonstrate classification accuracy of 91.75% with sensitivity of 88.23% and specificity of 96.48% at Q = 2.

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Classification of heart sound based on s-transform and neural network

Cited by 22 publications

References 4 publications

Diagnostic spectral segregation of cardiac sounds features

Diagnostic spectral segregation of cardiac sounds features

A robust heart sounds segmentation module based on S-transform

Detection of septal defects from cardiac sound signals using tunable-Q wavelet transform

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