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.

本论文提出一种基于 YOLOv8 算法的心律失常检测应用，通过在 MIT-BIH 数据集上的 fine-tuning，实现对单导联 ECG 信号的实时检测，可持续监测，检测时间为 0.002 秒，mAP@50 达到 0.961，能够为家庭用户提供直观的模型输出，实现了实时心律失常检测，具有推广至医疗行业的潜力。

使用 YOLOv8 进行实时心律失常检测的新应用

A novel application for real-time arrhythmia detection using YOLOv8

Due to rapidly rising healthcare costs worldwide, there is significant
interest in controlling them. An important aspect concerns price transparency,
as preliminary efforts have demonstrated that patients will shop for lower
costs, driving efficiency. This requires the data to be made available, and
models that can predict healthcare costs for a wide range of patient
demographics and conditions. We present an approach to this problem by
developing a predictive model using machine-learning techniques. We analyzed
de-identified patient data from New York State SPARCS (statewide planning and
research cooperative system), consisting of 2.3 million records in 2016. We
built models to predict costs from patient diagnoses and demographics. We
investigated two model classes consisting of sparse regression and decision
trees. We obtained the best performance by using a decision tree with depth 10.
We obtained an R-square value of 0.76 which is better than the values reported
in the literature for similar problems.

利用机器学习技术基于患者诊断和人口统计学数据构建预测模型，并使用决策树方法进行预测，其结果优于先前研究的类似问题报告的结果，因此有望降低医疗保健成本。