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In a landscape characterized by heightened connectivity and mobility, coupledwith a surge in cardiovascular ailments, the imperative to curtail healthcareexpenses through remote monitoring of cardiovascular health has become morepronounced. The accurate detection and classification of cardiac arrhythmiasare pivotal for diagnosing individuals with heart irregularities. This studyunderscores the feasibility of employing electrocardiograms (ECG) measurementsin the home environment for real-time arrhythmia detection. Presenting a freshapplication for arrhythmia detection, this paper leverages the cutting-edgeYou-Only-Look-Once (YOLO)v8 algorithm to categorize single-lead ECG signals. Weintroduce a novel loss-modified YOLOv8 model, fine-tuned on the MIT-BIHarrhythmia dataset, enabling real-time continuous monitoring. The obtainedresults substantiate the efficacy of our approach, with the model attaining anaverage accuracy of 99.5% and 0.992 mAP@50, and a rapid detection time of 0.002seconds on an NVIDIA Tesla V100. Our investigation exemplifies the potential ofreal-time arrhythmia detection, enabling users to visually interpret the modeloutput within the comfort of their homes. Furthermore, this study lays thegroundwork for an extension into a real-time explainable AI (XAI) model capableof deployment in the healthcare sector, thereby significantly advancing therealm of healthcare solutions.

A Novel real-time arrhythmia detection model using YOLOv8