Automated Rheumatic Heart Disease Detection from Phonocardiogram in Cardiology Ward

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

doi:10.5220/0009367108390844

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“…When perceptual features were combined with wavelet features [ 28 ], the classification performance was improved further. The best results were achieved by the different combinations of time, frequency, and perceptual features [ 52 , 53 ].…”

Section: Resultsmentioning

confidence: 99%

“…Only a single 30 s record from each subject was used in the analysis. Additional information about the dataset can be found in [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…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 ]. Their features consisted of the time domain, frequency domain, and perceptual domain features.…”

Section: Introductionmentioning

confidence: 99%

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Rheumatic Heart Disease Screening Based on Phonocardiogram

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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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Section: Resultsmentioning

confidence: 99%

“…Only a single 30 s record from each subject was used in the analysis. Additional information about the dataset can be found in [ 52 ].…”

Section: Methodsmentioning

confidence: 99%