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.

翻译：在高度互联与移动化的背景下，伴随心血管疾病激增，通过远程监测心血管健康以降低医疗费用的需求愈发迫切。准确检测与分类心律失常对于诊断心律不齐个体至关重要。本研究论证了在家庭环境中利用心电图（ECG）测量实现实时心律失常检测的可行性。本文提出一种全新的心律失常检测应用，利用先进的目标检测算法YOLOv8（You-Only-Look-Once v8）对单导联ECG信号进行分类。我们引入了一种改进损失函数的YOLOv8模型，并在MIT-BIH心律失常数据集上进行微调，从而实现实时连续监测。实验结果证实了该方法的效果：模型在NVIDIA Tesla V100上达到平均准确率99.5%、mAP@50为0.992，检测速度仅0.002秒。本研究表明实时心律失常检测的潜力，使用户可在居家环境中直观解读模型输出。此外，本研究为拓展至医疗领域可部署的实时可解释人工智能（XAI）模型奠定了基础，从而显著推进医疗解决方案的发展。