Heart Sound Segmentation toward Automated Heart Murmur Classification in Pediatric Patents

Kang, Sukryool; Doroshow, Robin Winkler; McConnaughey, James; Khandoker, Ahsan H.; Shekhar, Raj

doi:10.1109/sip.2015.11

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…The frequency of Still's murmur varies between 90 and 170 Hz, and pathological murmurs are in the range of 80–500 Hz ( 32 ). The next step was the application of a previously reported segmentation technique that identified S1 and S2 sound lobes in the recording and used them to identify individual cardiac cycles ( 18 , 30 ). We defined a cardiac cycle as the period between the onsets of two consecutive S1 sounds.…”

Section: Methodsmentioning

confidence: 99%

“…The focus of our work has been automated algorithmic identification of Still's murmur, specifically. We have reported an algorithm for segmenting cardiac cycles and identifying Still's murmur using traditional machine learning ( 18 , 30 ). With the acquisition of additional data, we describe here a deep-learning algorithm based on a CNN framework.…”

Section: Introductionmentioning

confidence: 99%

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Automated identification of innocent Still's murmur using a convolutional neural network

Shekhar

Vanama²,

John

et al. 2022

Front. Pediatr.

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BackgroundStill's murmur is the most prevalent innocent heart murmur of childhood. Auscultation is the primary clinical tool to identify this murmur as innocent. Whereas pediatric cardiologists routinely perform this task, primary care providers are less successful in distinguishing Still's murmur from the murmurs of true heart disease. This results in a large number of children with a Still's murmur being referred to pediatric cardiologists.ObjectivesTo develop a computer algorithm that can aid primary care providers to identify the innocent Still's murmur at the point of care, to substantially decrease over-referral.MethodsThe study included Still's murmurs, pathological murmurs, other innocent murmurs, and normal (i.e., non-murmur) heart sounds of 1,473 pediatric patients recorded using a commercial electronic stethoscope. The recordings with accompanying clinical diagnoses provided by a pediatric cardiologist were used to train and test the convolutional neural network-based algorithm.ResultsA comparative analysis showed that the algorithm using only the murmur sounds recorded at the lower left sternal border achieved the highest accuracy. The developed algorithm identified Still's murmur with 90.0% sensitivity and 98.3% specificity for the default decision threshold. The area under the receiver operating characteristic curve was 0.943.ConclusionsStill's murmur can be identified with high accuracy with the algorithm we developed. Using this approach, the algorithm could help to reduce the rate of unnecessary pediatric cardiologist referrals and use of echocardiography for a common benign finding.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated identification of innocent Still's murmur using a convolutional neural network

Shekhar

Vanama²,

John

et al. 2022

Front. Pediatr.

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“…In [23], PCG signals were denoised using the maximally flat magnitude (Butterworth) filter. The authors in [24][25][26][27] applied wavelet transformation (WT), a well-known denoising technique to identify true PCG signal components. Another PCG signal denoising method can be achieved via EMD, where complicated data are decomposed into a finite-small number of components [28].…”

Section: Pcg Signal Preprocessing Denoising and Enhancingmentioning

confidence: 99%

“…Choi and Jiang made a comparative study about the most used envelope-based methods: Shannon energy, Hilbert transform, and the casdiac Sound characteristic waveform (CSCW) [31]. Shannon energy and entropy envelope was used by [25,26,[32][33][34][35][36]. Other techniques use envelope extraction based on WT to gain the frequency characteristics of of S1 and S2 sound components [15].…”

Section: Pcg Signal Segmentationmentioning

confidence: 99%

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

Boulares

Alotaibi

Almansour

et al. 2021

IJERPH

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Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity.

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“…The digitalized signal is then analysed through an algorithm of choice. The analysis pipeline of the vast majority of all the CAD systems that Denoising techniques (step 1) range from linear filters (such as a combination of lowpass and high-pass filters) [12][13][14][15], wavelets-based methods [15,16] and Kalman filters [17]; signal segmentation includes include ML-based methods [15], while feature extraction leverages empirical mode decomposion (EMD) [18] or information theory-based methods (such as entropy and Shannon-information) [19] and hidden Markov models [20]. Techniques employed for feature extraction (step 2) range from discrete cosine transforms (DCT) [21], discrete wavelet transforms (DWT) [22][23][24], short-time Fourier transforms (STFT) [21,25], mel-frequency cepstrum coefficients (MFCC) [26,27] and Choi-Williams distributions (CWD) [25].…”

Section: (B) Automated Phonocardiogram Processing For Computer-aided Diagnosismentioning

confidence: 99%

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms

Duggento

Conti

Guerrisi

et al. 2021

Phil. Trans. R. Soc. A.

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Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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Heart Sound Segmentation toward Automated Heart Murmur Classification in Pediatric Patents

Cited by 8 publications

References 6 publications

Automated identification of innocent Still's murmur using a convolutional neural network

Automated identification of innocent Still's murmur using a convolutional neural network

Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms

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