Detection and Boundary Identification of Phonocardiogram Sounds Using an Expert Frequency-Energy Based Metric

Naseri, H.; Homaeinezhad, Mohammad Reza

doi:10.1007/s10439-012-0645-x

Cited by 95 publications

(51 citation statements)

References 26 publications

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“…CT_MBS proposed in this paper is an extension to multi-level basis selection (MLBS) [18] that tries to make use of most of the information laid throughout the trapezoidal sub-tree. MLBS defines an…”

Section: Cumulant-based Trapezoidal Multilevel Basis Selection (Ct_mbs)mentioning

confidence: 99%

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Cumulant-based trapezoidal basis selection for heart sound classification

Safara

2015

Med Biol Eng Comput

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Past decades witnessed the expansion of linear signal processing methods in numerous biomedical applications. However, the nonlinear behavior of biomedical signals revived the interest in nonlinear signal processing methods such as higher-order statistics, in particular higher-order cumulants (HOC). In this paper, HOC are utilized toward heart sound classification. Heart sounds are presented by wavelet packet decomposition trees. Information measures are then defined based on HOC of wavelet packet coefficients, and three basis selection methods are proposed to prune the trees and preserve the most informative nodes for feature extraction. In addition, an approach is introduced to reduce the dimensionality of the search space from the whole wavelet packet tree to a trapezoidal sub-tree of it. This approach can be recommended for signals with a short frequency range. HOC features are extracted from the coefficients of selected nodes and fed into support vector machine classifier. Experimental data is a set of 59 heart sounds from different categories: normal heart sounds, mitral regurgitation, aortic stenosis, and aortic regurgitation. The promising results achieved indicate the capabilities of HOC of wavelet packet coefficients to capture nonlinear characteristics of the heart sounds to be used for basis selection.

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Section: Cumulant-based Trapezoidal Multilevel Basis Selection (Ct_mbs)mentioning

confidence: 99%

“…In order to segment an ECG signal, a metric is defined. The metric is the amplitude threshold determined experimentally to differentiate R-peak from other events in the ECG signal [18]. Then, information about the location of R-peaks and frequency sampling rate of the signals were fused to find the onset of the S1.…”

Section: Preprocessingmentioning

confidence: 99%

Cumulant-based trapezoidal basis selection for heart sound classification

Safara

2015

Med Biol Eng Comput

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“…To this end many PCG heart sound segmentation methods have been developed, enabling the detection of fundamental heart sound markers such as the beginning/end of S1, systole, S2 and diastole. These segmentation techniques use different approaches such as signal envelopes [4][5], frequency and amplitude features [6] as well as phase [7] and complexity features [8], hidden markov models (HMM) [9][10], and machine learning approaches [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Identification of Abnormal Heart Sounds

Yazdani

Schlatter

Atyabi

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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As a part of 2016 Physionet/CinC callenge, this work aims at the detection of abnormal phonocardiogram (PCG) recordings. Heart sound signal analysis has been an active research topic over the past decades with various studies such as heart sound segmentation and classification. We used the Physionet/CinC2016 challenge PCG database, which contains a large public collection of PCG recordings from a variety of clinical and nonclinical environments. The PCG classification in this work is performed in two steps. PCG heartbeats are first segmented and various heart sound markers are delineated. Then, a series of beat-specific features are extracted from the segmented heartbeats. Finally, PCG recordings are classified into normal and abnormal groups by performing classification based on tape-long features and by analyzing beatextracted features from the PCG. Our method achieved an overall score of 80 in the unofficial phase of the challenge. In the official phase, the overall score of the proposed method was 82, with a sensitivity of 89%. IntroductionMechanical activity of the heart, which is triggered by the propagation of electrical activity within the heart, is audible at different locations on the chest wall. Phonocardiogram (PCG) is an audio recording of these mechanical activities recorded at the chest surface. Heat sounds provide valuable information which can help in the diagnosis of heart valve disorders. PCG comprises fundamental heart sounds from which S1 and S2 are the most prominent in normal recordings, respectively representing the beginning of ventricular contraction and the beginning of diastole [1]. In abnormal cases, various markers maybe present beside the fundamental heart sounds such as murmurs. Murmurs are noise-like high frequency sounds and long systolic murmurs as well as diastolic and continuous murmurs are generally pathologic [2].Auscultation is a common cost-effective technique that provides valuable information about heart valve problems. However, by listening to mechanical activity of the heart with a stethoscope, physicians cannot examine all physical characteristics of heart sounds, as it has been shown that only limited activity of heart sounds and murmurs are within human audibility range [1] [3].Over the years, various heart sound classification methods have been proposed. Generally, the analysis of heart sounds is performed on the heart cycle. To this end many PCG heart sound segmentation methods have been developed, enabling the detection of fundamental heart sound markers such as the beginning/end of S1, systole, S2 and diastole. These segmentation techniques use different approaches such as signal envelopes With the segmented cardiac cycles, the classification of heart sound pathologies is made possible and several methods have been proposed over the last decades. Among these studies, artificial neural networks [13], support vector machines [14] and HMM based [15] approaches are common. Classification based on clustering has also been shown to be effective in heart sound...

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“…PCG signal processing is one of the active research areas [1]- [3]. A considerable body of these researches is dedicated to heart sound classification.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

Safara¹,

Doraisamy²,

Azman³

et al. 2013

IJBBB

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Abstract-Numerous studies are being conducted in recent years focusing on phonocardiographic (PCG) signals due to their capability to characterize heart sounds. These characteristics can be exploited in developing computer-aided auscultation system as a complementary tool for clinicians in diagnosis of cardiovascular disorders. This study proposes a new type of features to distinguish five categories of heart sounds, including normal, mitral stenosis, mitral regurgitation, aortic stenosis, and aortic regurgitation. PCG signals were collected from online resources and training CDs. Wavelet packet transform was utilized for heart sound analysis as opposed to discrete wavelet transform that has been extensively used in the previous studies. Then, trapezoidal function was calculated for deriving feature vectors. A hybrid classifier was designed composing of three types of classifiers, multilayer perceptron (MLP) artificial neural network, k-nearest neighbor (KNN), and support vector machine (SVM), to classify feature vectors. The promising results demonstrate the effectiveness of the proposed trapezoidal features and hybrid classifier for heart sound classification.

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Detection and Boundary Identification of Phonocardiogram Sounds Using an Expert Frequency-Energy Based Metric

Cited by 95 publications

References 26 publications

Cumulant-based trapezoidal basis selection for heart sound classification

Cumulant-based trapezoidal basis selection for heart sound classification

Identification of Abnormal Heart Sounds

Diagnosis of Heart Valve Disorders through Trapezoidal Features and Hybrid Classifier

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