Analysis of the second heart sound using continuous wavelet transform

Debbal, S. M.; Bereksi–Reguig, F.

doi:10.1080/0309190031000111362

Cited by 41 publications

(22 citation statements)

References 7 publications

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“…4,5,7,10,15 Similar to these studies, the diagnoses of the various murmurs were known prior and the utility of the described algorithm in records from patients with unknown diagnosis is currently untested. A blind study to assess the algorithm utility is therefore required.…”

Section: Discussionmentioning

confidence: 95%

“…(1) To calculate the probability P(O | Mi) of the input signals in each model, (2) To arrange the output probabilities from maximum to minimum, (3) To select the maximum probability as the final output for this model, (4) To determine the best model with maximum probability amongst a series of heart models.…”

Section: Feature Classificationmentioning

confidence: 99%

“…For each frame, a Hamming window was multiplied with a FFT. The power spectrum was then warped to the Mel scale by Equation (4). Thereafter, 20 sets of triangular bandpass filters were used.…”

Section: Feature Extractionmentioning

confidence: 99%

“…13 With the introduction of echocardiography, studies have suggested that reliable diagnosis of heart valve diseases are attainable. 4,15 However, echocardiography is generally associated with relative high costs and is performed by cardiologists and internists only. Therefore, all patients suspected of having any heart valve disease are referred from the general practitioner to a specialist.…”

Section: Introductionmentioning

confidence: 99%

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Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model

et al. 2006

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Auscultation is an important diagnostic indicator for cardiovascular analysis. Heart sound classification and analysis play an important role in the auscultative diagnosis. This study uses a combination of Mel-frequency cepstral coefficient (MFCC) and hidden Markov model (HMM) to efficiently extract the features for pre-processed heart sound cycles for the purpose of classification. A system was developed for the interpretation of heart sounds acquired by phonocardiography using pattern recognition. The task of feature extraction was performed using three methods: time-domain feature, short-time Fourier transforms (STFT) and MFCC. The performances of these feature extraction methods were then compared. The results demonstrated that the proposed method using MFCC yielded improved interpretative information. Following the feature extraction, an automatic classification process was performed using HMM. Satisfactory classification results (sensitivity > or =0.952; specificity > or =0.953) were achieved for normal subjects and those with various murmur characteristics. These results were based on 1398 datasets obtained from 41 recruited subjects and downloaded from a public domain. Constituents characteristics of heart sounds were also evaluated using the proposed system. The findings herein suggest that the described system may have the potential to be used to assist doctors for a more objective diagnosis.

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Section: Discussionmentioning

confidence: 95%

Section: Feature Classificationmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model

et al. 2006

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“…After localization of the split, CWT gives a graphical representation that provides a quantitative analysis simultaneously in time and frequency. It is therefore very helpful in extracting clinically useful information [18].…”

Section: Measurement Of the S2 Splitmentioning

confidence: 99%

Analysis and study of the variation of splitting in the second heartbeat sound of wavelet transform

Debbal

Bereksi–Reguig

2006

Journal of Medical Engineering & Technology

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The second heart sound, S2, consists of two acoustic components, A2 and P2. The former is due to the closure of the aortic valve and the latter is due to the closure of the pulmonary valve. The aortic valve usually closes before the pulmonary valve, introducing a time delay known as the 'split'. A technique based on discrete wavelet transform (DWT) and continuous wavelet transform (CWT) is developed in this paper to measure the split. To quantify splitting, two components in S2 (i.e. A2 and P2) are identified, and the delay between the two components can be estimated. One normal case and three pathological cases (mitral stenosis, pulmonary stenosis and atrial septal defect) are considered in this study. The split is measured for each S2 sound of the considered signals. The split normally varies in duration over the cardiac cycle. In certain pathologies such as ASD (atrial septal defect) or PS (pulmonary stenosis), the split becomes fixed over the cardiac cycle. The main part of this paper consists of the identification and measurement of the S2 split. The study confirms the notion of 'variable splitting' for normal phonocardiogram and 'fixed splitting' for ASD and PS cases. This paper relates also to the establishment of statistical parameters to make a distinction between normal and pathological cases of phonocardiogram signals.

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Mitral Regurgitation PCG-Signal Classification based on Adaptive Db-Wavelet

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Analysis of the second heart sound using continuous wavelet transform

Cited by 41 publications

References 7 publications

Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model

Phonocardiographic Signal Analysis Method Using a Modified Hidden Markov Model

Analysis and study of the variation of splitting in the second heartbeat sound of wavelet transform

Mitral Regurgitation PCG-Signal Classification based on Adaptive Db-Wavelet

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