An efficient heart murmur recognition and cardiovascular disorders classification system

Ahmad, Maqsood; Mir, Junaid; Ullah, Muhammad Obaid; Shahid, Muhammad Laiq Ur Rahman; Syed, Muhammad Adnan

doi:10.1007/s13246-019-00778-x

Cited by 40 publications

(21 citation statements)

References 22 publications

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“…In order to help in the process of discrimination, Computer Aided Detection (CAD) systems were developed. (7) The method can detect very efficiently (average accuracy of 92.6%) (7) the heart murmur and classify it as innocent or associated to a cardiovascular disease.…”

Section: Discussionmentioning

confidence: 99%

When Clinical Judgement of a Heart Murmur is Wrong: A Case Report

Pop¹,

Butnariu

2020

Acta Medica Transilvanica

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Background: Infants with congenital heart defects (CHD) are at risk of death if not recognized and treated on time, desirable during intrauterine life or soon after birth. Case: A 6-week old infant presented for the evaluation of an innocent murmur, discovered immediately after birth and recorded by the neonatologist, family physician and pediatrician at follow-up visit. At physical examination, a grade 2/6 systolic murmur and tachycardia were observed. The electrocardiography revealed sinus tachycardia. Transthoracic echocardiography recorded multiple heart defects (three ventricular septal defects, an atrial septal defect, valvular regurgitations, impaired right heart function) and a vascular defect. Treatment consisted of surgical repair. Conclusions: The rarity of the case results from the atypical presentation of the cardiovascular defects only through an innocent heart murmur, therefore criteria requested by guidelines for evaluation through transthoracic echocardiography for the suspicion of a CHD, were not encountered and the diagnosis was delayed.

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Section: Discussionmentioning

confidence: 99%

When Clinical Judgement of a Heart Murmur is Wrong: A Case Report

Pop¹,

Butnariu

2020

Acta Medica Transilvanica

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“…Mel-frequency cepstral coefficients (MFCCs), a well-known group of features for speech/speaker recognition systems, have recently gained importance as features for classifying heart sounds [ 26 , 32 , 59 , 60 ]. Mel frequencies are grounded in the nonlinear physiognomies of the human ear’s sensitivity to different frequencies [ 61 ].…”

Section: Methodsmentioning

confidence: 99%

“…The method showed sensitivity and specificity of 82% and 88% respectively. An efficient method for the detection of abnormal PCG signals was proposed [ 32 ] using MFCCs and SVM with a classification accuracy of 92.6%. Classification of CAD and non-CAD subjects from PCG and ECG [ 33 ] using a dual input neural network (DINN) achieved specificity, accuracy, and G-mean of 89.17%, 95.62%, and 93.69%, respectively.…”

Section: Introductionmentioning

confidence: 99%

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Aziz

Khan

Alhaisoni

et al. 2020

Sensors

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Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals.

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“…Heart murmurs are defined as sustained audible noises that are produced during the cardiac-beat cycle. 4 In the most recent studies, several techniques and methods have been proposed to study the PCG signal in order to provide an aiding tool to help clinicians diagnose heart problems. Meziani et al proposed a set of features using the Discrete Wavelet Transform (DWT) to discriminate between various heart sound signals.…”

Section: Introductionmentioning

confidence: 99%

Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

Berraih

Baakek

Debbal

2021

Polish Journal of Medical Physics and Engineering

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Phonocardiography is a technique for recording and interpreting the mechanical activity of the heart. The recordings generated by such a technique are called phonocardiograms (PCG). The PCG signals are acoustic waves revealing a wealth of clinical information about cardiac health. They enable doctors to better understand heart sounds when presented visually. Hence, multiple approaches have been proposed to analyze heart sounds based on PCG recordings. Due to the complexity and the high nonlinear nature of these signals, a computer-aided technique based on higher-order statistics (HOS) is employed, it is known to be an important tool since it takes into account the non-linearity of the PCG signals. This method also known as the bispectrum technique, can provide significant information to enhance the diagnosis for an accurate and objective interpretation of heart condition. The objective expected by this paper is to test in a preliminary way the parameters which can make it possible to establish a discrimination between the various signals of different pathologies and to characterize the cardiac abnormalities. This preliminary study will be done on a reduced sample (nine signals) before applying it subsequently to a larger sample. This work examines the effectiveness of using the bispectrum technique in the analysis of the pathological severity of different PCG signals. The presented approach showed that HOS technique has a good potential for pathological discrimination of various PCG signals.

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An efficient heart murmur recognition and cardiovascular disorders classification system

Cited by 40 publications

References 22 publications

When Clinical Judgement of a Heart Murmur is Wrong: A Case Report

When Clinical Judgement of a Heart Murmur is Wrong: A Case Report

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

Preliminary study in the analysis of the severity of cardiac pathologies using the higher-order spectra on the heart-beats signals

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