Characterization of rheumatic heart disease from electrocardiogram recordings

Melkamu Hunegnaw Asmare; Amsalu Tomas Chuma; Carolina Varon; Frehiwot Woldehanna; Luc Janssens; Bart Vanrumste

doi:10.1088/1361-6579/aca6cb

Characterization of rheumatic heart disease from electrocardiogram recordings

Physiol Meas. 2023 Mar 1;44(2). doi: 10.1088/1361-6579/aca6cb.

Authors

Melkamu Hunegnaw Asmare^{1

2}, Amsalu Tomas Chuma^{1

3}, Carolina Varon^{1

4}, Frehiwot Woldehanna², Luc Janssens¹, Bart Vanrumste¹

Affiliations

¹ eMedia Research Lab/STADIUS, Department of Electrical Engineering (ESAT), KU Leuven, Andreas Vesaliusstraat 13, B-3000 Leuven, Belgium.
² Addis Ababa University, Addis Ababa Institute of Technology, Center of Biomedical Engineering, Addis Ababa, Ethiopia.
³ Department of Software Engineering, College of Electrical and Mechanical Engineering, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
⁴ Microgravity Research Center, Université Libre de Bruxelles, B-1050 Brussels, Belgium.

PMID: 36595302
DOI: 10.1088/1361-6579/aca6cb

Abstract

Objective. Rheumatic Heart Disease (RHD) is one of the highly prevalent heart diseases in developing countries that can affect the pericardium, myocardium, or endocardium. Rheumatic endocarditis is a common RHD variant that gradually deteriorates the normal function of the heart valves. RHD can be diagnosed using standard echocardiography or listened to as a heart murmur using a stethoscope. The electrocardiogram (ECG), on the other hand, is critical in the study and identification of heart rhythms and abnormalities. The effectiveness of ECG to identify distinguishing signs of rheumatic heart problems, however, has not been adequately examined. This study addressed the possible use of ECG recordings for the characterization of problems of the heart in RHD patients.Approach. To this end, an extensive ECG dataset was collected from patients suffering from RHD (PwRHD), and healthy control subjects (HC). Bandpass filtering was used at the preprocessing stage. Each data was then standardized by removing its mean and dividing by its standard deviation. Delineation of the onsets and offsets of waves was performed using KIT-IBT open ECG MATLAB toolbox. PR interval, QRS duration, RR intervals, QT intervals, and QTc intervals were computed for each heartbeat. The median values of the temporal parameters were used to eliminate possible outliers due to missed ECG waves. The data were clustered in different age groups and sex. Another categorization was done based on the time duration since the first RHD diagnosis.Main results. In 47.2% of the cases, a PR elongation was observed, and in 26.4% of the cases, the QRS duration was elongated. QTc was elongated in 44.3% of the cases. It was also observed that 62.2% of the cases had bradycardia.Significance. The end product of this research can lead to new medical devices and services that can screen RHD based on ECG which could somehow assist in the detection and diagnosis of the disease in low-resource settings and alleviate the burden of the disease.

Keywords: PR elongation; QRS complex; QTc; electrocardiogram; heart rate; rheumatic heart disease.

MeSH terms

Echocardiography / methods
Electrocardiography
Heart Rate
Humans
Mass Screening / methods
Rheumatic Heart Disease* / diagnosis