  In a landscape characterized by heightened connectivity and mobility, coupled
with a surge in cardiovascular ailments, the imperative to curtail healthcare
expenses through remote monitoring of cardiovascular health has become more
pronounced. The accurate detection and classification of cardiac arrhythmias
are pivotal for diagnosing individuals with heart irregularities. This study
underscores the feasibility of employing electrocardiograms (ECG) measurements
in the home environment for real-time arrhythmia detection. Presenting a fresh
application for arrhythmia detection, this paper leverages the cutting-edge
You-Only-Look-Once (YOLO)v8 algorithm to categorize single-lead ECG signals. We
introduce a novel loss-modified YOLOv8 model, fine-tuned on the MIT-BIH
arrhythmia dataset, enabling real-time continuous monitoring. The obtained
results substantiate the efficacy of our approach, with the model attaining an
average accuracy of 99.5% and 0.992 mAP@50, and a rapid detection time of 0.002
seconds on an NVIDIA Tesla V100. Our investigation exemplifies the potential of
real-time arrhythmia detection, enabling users to visually interpret the model
output within the comfort of their homes. Furthermore, this study lays the
groundwork for an extension into a real-time explainable AI (XAI) model capable
of deployment in the healthcare sector, thereby significantly advancing the
realm of healthcare solutions.
