Detection of cardiac abnormality from PCG signal using LMS based least square SVM classifier

Ari, Samit; Hembram, Koushik; Saha, Goutam

doi:10.1016/j.eswa.2010.05.088

Cited by 136 publications

(73 citation statements)

References 26 publications

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“…The ability to distinguish pathological and normal heart murmurs may provide valuable information about a potential diagnosis. Another example of diagnosis-oriented classification is the method proposed by Reed et al for the classification of heart acoustics signals based on a least square support vector machine (LSSVM) using a wavelet-based feature set (38). In this paper, an ANN-based method for determining the diagnosis for a specific condition, mitral valve prolapse, is proposed .…”

Section: Discussionmentioning

confidence: 99%

“…An ANN-based classifier can be used for heart sound analysis, where different approaches may precede the classification process, such as the previously described wavelet representation (37). The phonocardiograms (38) were subjected to a fast Fourier transform to extract the energy spectrum in the frequency domain to detect heart murmurs in children. The processed signals were used to develop statistical classifiers and a classifier based on ANN.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Discussionmentioning

confidence: 99%

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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“…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 pathology classification [16].…”

Section: Introductionmentioning

confidence: 99%

“…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 pathology classification [16].…”

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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“…A number of researchers have also applied support vector machines (SVM) for heart sound classification in recent years. The studies can also be divided into different groups according to the feature extraction methods, including wavelet [10], time, frequency and time-frequency feature-based classifiers [11]. Hidden Markov models (HMM) have also been employed for pathology classification in PCG recordings [5].…”

Section: Introductionmentioning

confidence: 99%