An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases

Sepehri, Amir; Kocharian, Armen; Janani, Azin S.; Gharehbaghi, Arash

doi:10.1007/s10916-015-0359-3

Cited by 26 publications

(26 citation statements)

References 33 publications

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“…MFCCs were widely employed by several studies [ 9 , 11 , 17 , 35 ], and acted as baseline features of choice. The SVM classifier is also widely adopted by existing works [ 10 , 11 , 12 , 13 ].…”

Section: Discussionmentioning

confidence: 99%

“…PCG signal classification was achieved through linear SVM and a combination of dynamic time wrapping (DTW) and Mel-frequency cepstral coefficient (MFCC) features in [ 11 ] to achieve 82.4% accuracy. The screening method of PCG signals using a modified Arash-band method and an SVM classifier has been used [ 12 ]. In [ 13 ], the PCG signal was first segmented into S1, systole, S2, and diastole through the hidden Markov model (HMM).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…This shows the unrealistically good score, which will not be achieved in practice on unseen patients. In practice, the algorithm is expected to perform on unseen patients [18,[21][22][23][24]34]. However, these results can indicate the performance of the model for patients with follow-ups.…”

Section: Importance Of Correct Model Selection and Evaluation Frameworkmentioning

confidence: 99%

“…An ML can accurately diagnose PCGs compared in the 3-class classification problem (no murmurs, innocent murmurs, and pathologic murmurs) on a cohort of 106 children with an average age of 8 years old has been demonstrated in [21]. The frequency band analysis of paediatric PCG with SVM for murmur characterisation was performed in [22], with the focus on the Android app development towards clinical usage of the method.…”

Section: Introductionmentioning

confidence: 99%

A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

et al. 2021

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The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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“…Several studies have successfully used the discrete wavelet transform in the analysis of pathological severity of aortic and mitral diseases 40. New intelligent computer-aided diagnosis systems (Intelligent Phonocardiography) based on heart sound signal analysis are used to diagnose various heart diseases (atrial fibrillation, aortic regurgitation, mitral regurgitation, normal sound, pulmonary stenosis, ventricular septal defect, pediatric heart diseases, and assessment of aortic valve stenosis) 41–43…”

Section: The Present and Futurementioning

confidence: 99%

<p>The first 200 years of cardiac auscultation and future perspectives</p>

Montinari¹,

Minelli

2019

JMDH

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Cardiac auscultation – even with its limitations – is still a valid and economical technique for the diagnosis of cardiovascular diseases, and despite the growing demand for sophisticated imaging techniques, clinical use of the stethoscope in medical practice has not yet been abandoned. In 1816, René-Théophile-Hyacinthe Laënnec invented the stethoscope, while examining a young woman with suspected heart disease, giving rise to mediated auscultation. He described in detail several heart and lung sounds, correlating them with postmortem pathology. Even today, a correct interpretation of heart sounds, integrated with the clinical history and physical examination, allows to detect properly most of the structural heart abnormalities or to evaluate them in a differential diagnosis. However, the lack of organic teaching of auscultation and its inadequate practice have a negative impact on the clinical competence of physicians in training, also reflecting a diminished academic interest in physical semiotic. Medical simulation could be an effective instructional tool in teaching and deepening auscultation. Handheld ultrasound devices could be used for screening or for integrating and improving auscultatory abilities of physicians; the electronic stethoscope, with its new digital capabilities, will help to achieve a correct diagnosis. The availability of innovative representations of the sounds with phono- and spectrograms provides an important aid in diagnosis, in teaching practice and pedagogy. Technological innovations, despite their undoubted value, must complement and not supplant a complete physical examination; clinical auscultation remains an important and cost-effective screening method for the physicians in cardiorespiratory diagnosis. Cardiac auscultation has a future, and the stethoscope has not yet become a medical heirloom.

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An Intelligent Phonocardiography for Automated Screening of Pediatric Heart Diseases

Cited by 26 publications

References 33 publications

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

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

A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

<p>The first 200 years of cardiac auscultation and future perspectives</p>

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