In recent years, there has been an increasing need to reduce healthcare costs in remote monitoring of cardiovascular health. Detecting and classifying cardiac arrhythmia is critical to diagnosing patients with cardiac abnormalities. This paper shows that complex systems such as electrocardiograms (ECG) can be applicable for at-home monitoring. This paper proposes a novel application for arrhythmia detection using the state-of-the-art You-Only-Look-Once (YOLO)v8 algorithm to classify single-lead ECG signals. A custom YOLOv8 model was fine-tuned on the MIT-BIH dataset to detect arrhythmia in real-time to allow continuous monitoring. Results show that our model can detect heartbeats with a mAP@50 of 0.961 with a detection time of 0.002s on an NVIDIA Tesla V100. Our study demonstrated the potential of real-time arrhythmia detection, where the model output can be visually interpreted for at-home users. Furthermore, this study could be extended into a real-time XAI model, deployed in the healthcare industry, and significantly advancing healthcare needs.

翻译：近年来，远程心血管健康监测中降低医疗成本的需求日益增长。检测与分类心律失常对于诊断心脏异常患者至关重要。本文表明，心电图等复杂系统可适用于家庭监测。本研究提出了一种创新应用，采用先进的YOLOv8（You-Only-Look-Once）算法对单导联心电图信号进行分类。通过在MIT-BIH数据集上对定制YOLOv8模型进行微调，实现了实时心律失常检测，从而支持连续监测。结果表明，该模型在NVIDIA Tesla V100上检测心跳的mAP@50达到0.961，检测时间为0.002秒。我们的研究证明了实时心律失常检测的潜力，其模型输出可被家庭用户直观解读。此外，本研究可扩展为实时可解释人工智能模型，部署于医疗行业，显著推进医疗需求的发展。