Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds

Asmare, Melkamu Hunegnaw; Woldehanna, Frehiwot; Janssens, Luc; Vanrumste, Bart

doi:10.1109/embc44109.2020.9176544

Cited by 12 publications

(8 citation statements)

References 6 publications

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“…Automatic diagnosis of echo-detected RHD is feasible and can form the core of the screening programs of the future covering the workload of experts [ 85 ]. Recent data has also shown promise for a convolutional neural network-based deep learning algorithm to identify heart sounds as ‘rheumatic’ with an overall accuracy of 96.1% having 94.0% sensitivity and 98.1% specificity [ 67 ]. Therefore, AI can form the backbone of future global screening programs for RHD.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

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The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002–2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians’ diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the ‘human touch’ limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Sethi

Patel²,

Kaka³

et al. 2022

JCM

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“…At last, 71 original articles were included. These studies can be broadly categorized into several groups: methods (15 papers, including heart sound segmentation [6, 7, 8, 9, 10, 11, 12, 13], noise cancellation [14, 15, 16], algorithm development [17, 18, 19], and database development [20]), cardiac murmurs detection (36 papers [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56]), valvular heart disease (6 papers [57, 58, 59, 60, 61, 62]), congenital heart disease (4 papers [63, 64, 65, 66]), heart failure (4 papers [67, 68, 69, 70]), coronary artery disease (2 papers [71, 72]), rheumatic heart disease (2 papers [73, 74]), and extracardiac applications (2 papers [75, 76]).…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Heart Sound Analysis: A Literature Review

Zhao,

Geng,

Wang

et al. 2023

Preprint

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Heart sound auscultation is a physical examination routinely used in clinical practice to identify potential cardiac abnormalities. However, accurate interpretation of heart sounds requires specialized training and experience, thereby limiting its generalizability. Deep learning, a subset of machine learning, involves training artificial neural networks to learn from large datasets and perform complex tasks related to intricate patterns, such as disease diagnosis, event prediction, and clinical decision-making. Over the past decade, deep learning has been successfully applied to heart sound analysis with remarkable achievements. Meanwhile, as heart sound analysis is gaining attention, many public and private heart sound datasets have been established for model training. The massive accumulation of heart sound data improves the performance of deep learning-based heart sound models and extends their clinical application scenarios. In this review, we will compile the commonly used datasets in heart sound analysis, introduce the fundamentals and state-of-the-art techniques in heart sound analysis and deep learning, and summarize the current applications of deep learning for heart sound analysis and their limitations for future improvement.

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“…Feature extraction for the heart sound classification task was another domain that was heavily explored. The frequently used features for heart sound classification were time domain features [ 37 , 38 ]; frequency domain features [ 39 , 40 , 41 ]; spectrogram features [ 42 , 43 , 44 , 45 ]; a combination of time, frequency and perceptual features [ 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 ]. A combination of perceptual and wavelet features has also been used [ 28 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

“…After that, Asmare M. H. et al [ 44 , 52 ] also proposed heart sound classification to detect murmur from RHD. They extracted 26 features from the entire heart sound signal to properly deal with systolic as well as diastolic murmurs caused by RHD [ 52 ].…”

Section: Introductionmentioning

confidence: 99%

Rheumatic Heart Disease Screening Based on Phonocardiogram

Asmare

Filtjens

Woldehanna

et al. 2021

Sensors

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Rheumatic heart disease (RHD) is one of the most common causes of cardiovascular complications in developing countries. It is a heart valve disease that typically affects children. Impaired heart valves stop functioning properly, resulting in a turbulent blood flow within the heart known as a murmur. This murmur can be detected by cardiac auscultation. However, the specificity and sensitivity of manual auscultation were reported to be low. The other alternative is echocardiography, which is costly and requires a highly qualified physician. Given the disease’s current high prevalence rate (the latest reported rate in the study area (Ethiopia) was 5.65%), there is a pressing need for early detection of the disease through mass screening programs. This paper proposes an automated RHD screening approach using machine learning that can be used by non-medically trained persons outside of a clinical setting. Heart sound data was collected from 124 persons with RHD (PwRHD) and 46 healthy controls (HC) in Ethiopia with an additional 81 HC records from an open-access dataset. Thirty-one distinct features were extracted to correctly represent RHD. A support vector machine (SVM) classifier was evaluated using two nested cross-validation approaches to quantitatively assess the generalization of the system to previously unseen subjects. For regular nested 10-fold cross-validation, an f1-score of 96.0 ± 0.9%, recall 95.8 ± 1.5%, precision 96.2 ± 0.6% and a specificity of 96.0 ± 0.6% were achieved. In the imbalanced nested cross-validation at a prevalence rate of 5%, it achieved an f1-score of 72.2 ± 0.8%, recall 92.3 ± 0.4%, precision 59.2 ± 3.6%, and a specificity of 94.8 ± 0.6%. In screening tasks where the prevalence of the disease is small, recall is more important than precision. The findings are encouraging, and the proposed screening tool can be inexpensive, easy to deploy, and has an excellent detection rate. As a result, it has the potential for mass screening and early detection of RHD in developing countries.

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Rheumatic Heart Disease Detection Using Deep Learning from Spectro-Temporal Representation of Un-segmented Heart Sounds

Cited by 12 publications

References 6 publications

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Artificial Intelligence in Pediatric Cardiology: A Scoping Review

Deep Learning for Heart Sound Analysis: A Literature Review

Rheumatic Heart Disease Screening Based on Phonocardiogram

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