Rheumatic Heart Disease Screening Based on Phonocardiogram

Abstract

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.

Development and Validation of a Radiomics Model Based on 3-Dimensional Endoanal Rectal Ultrasound of Rectal Cancer for Predicting Lymph Node Metastasis

“… 29 Nested cross-validation is widely used in machine learning and deep learning. 20 , 30 - 32 Compared with a simple cross-validation, nested cross-validation can reduce overfitting and limit optimism bias. Especially in relatively small samples, nested cross-validation procedure provides an almost unbiased estimate of the true error.…”

Heart sounds: Past, present, and future from a technological and clinical perspective – a systematic review

“…A 2021 Ethiopia-based study by Asmare et al proposed an automated Rheumatic heart disease (RHD) screening based on Phonocardiograms. 59 Utilizing the ThinkLabs One™ electronic stethoscope, this group collected heart sound data from 124 echocardiography-proved RHD patients and 127 healthy subjects. Based on 31 distinct features on the phonocardiogram, they were able to achieve a recall value of 95.8 ± 1.5%.…”

Characterization of rheumatic heart disease from electrocardiogram recordings

“…Given the importance of RHD as the cause of disability, the development of a simple method for mass screening programs has been of particular importance (Dougherty et al 2017). The use of a Phonocardiogram (PCG) as an RHD screening tool has been extensively studied (Springer 2015, Asmare et al 2021. PCG data is susceptible to noise from a variety of sources, and the art of manual auscultation is elusive and difficult to perfect.…”