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

Cardiovascular diseases (CVDs) are the number one cause of death worldwide.
While there is growing evidence that the atrial fibrillation (AF) has strong
associations with various CVDs, this heart arrhythmia is usually diagnosed
using electrocardiography (ECG) which is a risk-free, non-intrusive, and
cost-efficient tool. Continuously and remotely monitoring the subjects' ECG
information unlocks the potentials of prompt pre-diagnosis and timely
pre-treatment of AF before the development of any life-threatening
conditions/diseases. Ultimately, the CVDs associated mortality could be
reduced. In this manuscript, the design and implementation of a personalized
healthcare system embodying a wearable ECG device, a mobile application, and a
back-end server are presented. This system continuously monitors the users' ECG
information to provide personalized health warnings/feedbacks. The users are
able to communicate with their paired health advisors through this system for
remote diagnoses, interventions, etc. The implemented wearable ECG devices have
been evaluated and showed excellent intra-consistency (CVRMS=5.5%), acceptable
inter-consistency (CVRMS=12.1%), and negligible RR-interval errors (ARE<1.4%).
To boost the battery life of the wearable devices, a lossy compression schema
utilizing the quasi-periodic feature of ECG signals to achieve compression was
proposed. Compared to the recognized schemata, it outperformed the others in
terms of compression efficiency and distortion, and achieved at least 2x of CR
at a certain PRD or RMSE for ECG signals from the MIT-BIH database. To enable
automated AF diagnosis/screening in the proposed system, a ResNet-based AF
detector was developed. For the ECG records from the 2017 PhysioNet CinC
challenge, this AF detector obtained an average testing F1=85.10% and a best
testing F1=87.31%, outperforming the state-of-the-art.

本研究设计了一个个性化健康诊断系统，包括一个可穿戴式心电图设备、移动应用程序和后端服务器，可以对用户的心电信息进行持续监测，以提供个性化的健康警告 / 反馈，通过远程诊断和干预等方式支持与健康顾问的沟通。在此系统中，提出了一种利用心电信号准周期特征的有损信号压缩方法，设计并训练了一个基于 ResNet 的 AF 诊断器，该诊断器在测试中取得了较好的效果，F1 得分最高可达 87.31%。

基于佩戴式心电监测系统、ECG 有损压缩方案和基于 ResNet 的房颤检测器的个性化心脏人群医疗研究

Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector