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基于人工智能和物联网的优化设计对心电图监测系统的影响。

Influence of Optimization Design Based on Artificial Intelligence and Internet of Things on the Electrocardiogram Monitoring System.

机构信息

The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China.

Lenovo Research, Lenovo Group, Beijing 100094, China.

出版信息

J Healthc Eng. 2020 Oct 26;2020:8840910. doi: 10.1155/2020/8840910. eCollection 2020.

DOI:10.1155/2020/8840910
PMID:33178407
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7609146/
Abstract

With the increasing emphasis on remote electrocardiogram (ECG) monitoring, a variety of wearable remote ECG monitoring systems have been developed. However, most of these systems need improvement in terms of efficiency, stability, and accuracy. In this study, the performance of an ECG monitoring system is optimized by improving various aspects of the system. These aspects include the following: the judgment, marking, and annotation of ECG reports using artificial intelligence (AI) technology; the use of Internet of Things (IoT) to connect all the devices of the system and transmit data and information; and the use of a cloud platform for the uploading, storage, calculation, and analysis of patient data. The use of AI improves the accuracy and efficiency of ECG reports and solves the problem of the shortage and uneven distribution of high-quality medical resources. IoT technology ensures the good performance of remote ECG monitoring systems in terms of instantaneity and rapidity and, thus, guarantees the maximum utilization efficiency of high-quality medical resources. Through the optimization of remote ECG monitoring systems with AI and IoT technology, the operating efficiency, accuracy of signal detection, and system stability have been greatly improved, thereby establishing an excellent health monitoring and auxiliary diagnostic platform for medical workers and patients.

摘要

随着对远程心电图 (ECG) 监测的重视，各种可穿戴远程 ECG 监测系统已经得到了发展。然而，这些系统中的大多数在效率、稳定性和准确性方面都需要改进。在本研究中，通过改进系统的各个方面来优化 ECG 监测系统的性能。这些方面包括以下几个方面：使用人工智能 (AI) 技术对心电图报告进行判断、标记和注释；使用物联网 (IoT) 连接系统的所有设备并传输数据和信息；以及使用云平台上传、存储、计算和分析患者数据。AI 的使用提高了心电图报告的准确性和效率，并解决了高质量医疗资源短缺和分布不均的问题。IoT 技术确保了远程 ECG 监测系统在即时性和快速性方面的良好性能，从而保证了高质量医疗资源的最大利用效率。通过使用 AI 和 IoT 技术优化远程 ECG 监测系统，大大提高了系统的运行效率、信号检测的准确性和系统稳定性，从而为医务人员和患者建立了一个优秀的健康监测和辅助诊断平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/6d969f3507ce/JHE2020-8840910.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/8ca87df8263f/JHE2020-8840910.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/1b7accc82730/JHE2020-8840910.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/4bc1332ec88e/JHE2020-8840910.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/5dcb094891a2/JHE2020-8840910.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/6d969f3507ce/JHE2020-8840910.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/8ca87df8263f/JHE2020-8840910.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/1b7accc82730/JHE2020-8840910.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/4bc1332ec88e/JHE2020-8840910.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/5dcb094891a2/JHE2020-8840910.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bda1/7609146/6d969f3507ce/JHE2020-8840910.005.jpg

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