Automated Identification of Heart Arrhythmias through HRV Analysis and Machine Learning
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Date
2024
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NIGERIAN JOURNAL OF TECHNOLOGICAL DEVELOPMENT
Abstract
Sudden cardiac death and arrhythmia are responsible for about 15-20% of cardiovascular disease
incidences. Conventionally, the prediction and diagnosis of cardiovascular disorders (CVDs) have been mainly
through the evaluation of ECG patterns by cardiologists. To improve the accuracy of and automate this process, and
facilitate early detection, Heart Rate Variability (HRV) analysis has been promoted as a diagnostic and predictive tool
for CVDs. In the present study, a machine learning model capable of detecting the presence of arrhythmia, using HRV
indices obtained from ECG signals was built. Unlike similar works in the literature, this study deployed the developed
model on Raspberry Pi with Streamlit software. Two ECG datasets from the Physionet database, one with arrhythmia
patients (48 half-hour recordings) and another with healthy individuals (18 24-hour recordings), were employed. An
ensemble of seven different machine learning models was used on the two sets of datasets to classify ECG recordings
into Arrhythmia and Normal Sinus Rhythm (NSR). The best models were able to predict the presence of Arrhythmia
in a 3-minute recording with an accuracy of 95.96%, and in a 10-minute recording with an accuracy of 96.20%. These
performance measures were calculated using test dataset. The Random Forest models also had the highest precision,
AUC, (Area under the Curve) recall, and F1 scores compared to the other models tested. The highest performing model
(i.e., Random Forest Model) was then deployed onto a Raspberry Pi with Streamlit as the software interface for
usability. This was done to facilitate a smooth user experience for faster and seamless diagnoses for cardiologists
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Citation
Lawal, S. K., Muniru, I. O., Yahaya, S.A., and Ibitoye, M. O. (2024): Automated Identification of Heart Arrhythmias through HRV Analysis and Machine Learning, Nigerian Journal of Technological Development (NJTD), 21 (1); 73-84, Published by Faculty of Engineering & Technology, University of Ilorin, Ilorin, Nigeria.