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基于无线通信的心电生理信号远程监护系统
引用本文:宁文双,梁婷,YUANYongJ.基于无线通信的心电生理信号远程监护系统[J].西南交通大学学报,2016,29(1):193-200.
作者姓名:宁文双  梁婷  YUANYongJ
摘    要:为协助医师分析药物疗效和全面监控病人的康复过程,设计了移动医疗监护系统.该系统以生理信号之一的心电信号为例,基于无线通信技术进行监护终端的信号采集,使用高精度心电传感器通过ARM芯片控制心电信号采集,并利用GPRS网络和Internet网络将数据进行远程无线传输;在社区医院或远程监护中心,基于SOCKET套接字的网络应用程序,建立友好的可视化操作界面,在监护中心以SQL数据库对患者库和心电信息库进行组织和管理.对该系统进行了仿真实验,仿真结果显示,心电传感器采样频率为200 Hz时,满足奈奎斯特采样定理,采样精度达到了5 mV,能够将心电P、Q、R、S、T波形准确采样;使用ARM处理器可以高性能、低功耗、实时地对心电生理信号进行分析、报警;系统提供的心电波形回放和QRS波形检测等功能,可以扩展为相应的生理信号监护系统. 

关 键 词:ARM    数据库    GPRS    远程医疗
收稿时间:2014-04-28

Electrocardiography Monitoring System Based on Wireless Communication
NING Wenshuang,LIANG Ting,YUAN Yong J.Electrocardiography Monitoring System Based on Wireless Communication[J].Journal of Southwest Jiaotong University,2016,29(1):193-200.
Authors:NING Wenshuang  LIANG Ting  YUAN Yong J
Abstract:To assist the doctors in assessing drug effects and monitor the recovery of patients, the mobile medical monitoring system was designed in this paper, which utilizes electrocardiography (ECG) signals. ECG sensors were adopted to gather signals from monitoring sites. Both ECG signal acquisition and transmission were controlled by an ARM chip. GPRS network and Internet network were used for the remote wireless data transmission. In either a monitoring center or community hospital, SOCKET technology was utilized to develop network applications. The end-user friendly interface was visually established, and patient database and ECG database were administrated by the monitoring center with SQL database. When the sampling frequency of ECG sensors is 200 Hz, the Nyquist sampling theorem was satisfied and the acquisition precision is up to 5 mV, which can obtain accurate ECG P, Q, R, S, T waveforms. ARM processor has the merits of high performance, low power consumption and real-time analysis. The developed system not only realizes remote, real-time monitoring and alarming for ECG, but also provides the functions such as waveform playback and QRS waveform detection, which can be extended to other applications of physiological signal monitoring system. 
Keywords:
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