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Session: Wearable
Session starts: Friday 27 January, 11:30
Presentation starts: 12:00
Room: Room 531

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Amsalu Tomas Chuma Chuma (KU Leuven)
Carolina Varon (Université libre de Bruxelles)
Desalew Mekonnen (Addis Ababa University)
Melkamu Hunegnaw (Addis Ababa University)
Rik Willems (UZ Leuven, KU Leuven)
Bart Vanrumste (KU Leuven)

Abstract:
Rheumatic heart disease (RHD) is caused by untreated strep-throat infection from beta-hemolytic group-A streptococci that leads to cardiac valve damages. It mostly affects people at a younger age under 35 years. The health burden of RHD accounts for about one third of the annual cardiac morbidity in the Global South. Echocardiography examination is a gold standard for the diagnosis of RHD. This examination is often impractical in the Global South mainly because of the lack of cardiologists, and the limited echocardiograph machines available at health centers. As a result, RHD is an underdiagnosed disease that leads to an increased morbidity and mortality. One alternative to tackle these limitations, is to develop a more affordable system to detect RHD based on single-lead electrocardiogram (ECG) signals at cardiac wards in the Global South. A limited number of studies have focussed on the use of single-lead ECG for this task. Therefore, this study presents a potential use of single-lead wearable ECG sensors for automatic classification of late-stage RHD using a convolutional neural network (CNN). The CNN feature maps were combined with wavelet coefficients of the input signal to provide distinct spectral ranges of ECG waves. The experimental single-lead ECG dataset was recorded in one of the major cardiac referral hospitals in Ethiopia, which consists of 121 confirmed RHD and 45 normal subjects. Considering common cardiac disease cases in hospitals of the global south, age matched additional 400 subjects’ lead-I rhythms of normal sinus, ischemic, hypertrophic, myocardial infarction and conduction disturbance were added from the Physikalisch-Technische Bundesanstalt (PTB-XL) public dataset. The ratios of each arrhythmia were set to simulate cardiac wards in the global south. From each ECG recording, a slice of 10 seconds was resampled at 250Hz, denoised and then fed to the model. The 5-folds cross-validation performance shows an F1-score of 64.9%, precision of 66.2% and recall of 65%. The results demonstrate that single-lead ECG can be used as a detection tool for symptomatic RHD patients in vulnerable age groups among at-risk communities. This could enhance delivery of point-of-care healthcare in low-resource medical settings.