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ECG signal processing for long-term healthcare monitoring in body sensor networks.

机译:ECG信号处理可用于人体传感器网络中的长期医疗保健监视。

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摘要

This work focuses on body sensor networks (BSNs) based ECG signal processing for long-term healthcare monitoring. A body sensor network is a wireless sensor network consisting of various implantable or wearable biosensors and an external device as the network coordinator. Benefitting from the miniature-size and biocompatible wireless sensor nodes, body sensor networks can provide long-term, ubiquitous, and low-cost healthcare monitoring, with their interference to user's daily life reduced to the minimum.;Since most of the sensors are powered by batteries, energy efficiency is crucial for the lifetime and usability of a body sensor network. As Medium Access Control (MAC) is one of the most important factors that affect the energy efficiency of wireless communication, two energy-efficient MAC protocols, H-MAC and BSN-MAC are introduced in this work. H-MAC, a Time Division Multiple Access (TDMA) based medium access control protocol specially tailored for body sensor networks, aims to improve BSNs' energy efficiency by exploiting heartbeat rhythm to perform TDMA time synchronization. Using H-MAC, biosensors can achieve time synchronization without having to turn on their radios to receive periodic timing information from a central network coordinator, so that energy cost for time synchronization can be completely eliminated, and the network lifetime can be prolonged. The second MAC protocol proposed is BSN-MAC, which is an adaptive, feedback-based, and IEEE 802.15.4-compatible MAC protocol. It exploits the feedback information from the deployed sensors to form a closed-loop control of the MAC layer parameters. A control algorithm is proposed to enable the BSN coordinator to adjust parameters of the IEEE 802.15.4 superframe to achieve both high energy efficiency and low latency on energy critical nodes.;In the ECG signal processing section, we first propose a scheme that utilizes the body activity context information obtained from a body sensor network to help detect QRS complex, the most significant waveform in ECG signals, in a daily ambulatory environment. Body activity information is used to select the optimal leads and QRS complex detector, so that best QRS complex detection performance can be achieved under environments with different Signal Noise Ratios (SNRs). With the QRS complex located, a Hidden Markov Model (HMM) based technique is developed to perform further detailed ECG segmentation. In order to make HMMs adapt promptly to the temporal variations and reduce the misalignment errors, a body sensor network based active HMM parameter adaptation algorithm is presented. Instead of a single generic model, multiple individualized HMMs are used to improve the temporal adaptability. Once the ECG signals are segmented, clinical tests can be performed, such as Heart Rate Recovery (HRR) and ST segment depression analysis during Exercise Testing (ET). Then a Multivariate Autoregressive (MAR) based sensor fusion technique is introduced to improve the ECG processing reliability and accuracy by taking advantages of combining sensory data from heterogeneous biosensors in the network.;Since no satisfying multi-physiological parameter database is available to support the full spectrum study of body sensor networks, there is a need to build a customized hardware platform to collect biosignals. With the platform, various biosensors can communicate wirelessly and physiological parameters can be obtained from heterogeneous biosensors simultaneously and flexibly. A hardware platform designed for various biosignal acquisitions is discussed. The major components on the PCB and schematic are introduced.
机译:这项工作集中于基于身体传感器网络(BSN)的ECG信号处理,以进行长期医疗保健监控。人体传感器网络是一种无线传感器网络,由各种可植入或可穿戴生物传感器以及作为网络协调器的外部设备组成。得益于微型尺寸和生物兼容的无线传感器节点,人体传感器网络可以提供长期,无所不在和低成本的医疗保健监控,并将对用户日常生活的干扰降到最低。通过电池,能源效率对于人体传感器网络的寿命和可用性至关重要。由于媒体访问控制(MAC)是影响无线通信能效的最重要因素之一,因此在这项工作中引入了两种节能MAC协议H-MAC和BSN-MAC。 H-MAC是专门为身体传感器网络量身定制的基于时分多址(TDMA)的媒体访问控制协议,旨在通过利用心跳节奏执行TDMA时间同步来提高BSN的能效。使用H-MAC,生物传感器可以实现时间同步,而无需打开无线电以从中央网络协调器接收周期性的定时信息,从而可以完全消除时间同步的能源成本,并可以延长网络寿命。提出的第二个MAC协议是BSN-MAC,它是一种自适应的,基于反馈且与IEEE 802.15.4兼容的MAC协议。它利用来自已部署传感器的反馈信息来形成MAC层参数的闭环控制。提出了一种控制算法,以使BSN协调器能够调整IEEE 802.15.4超帧的参数,从而在能源关键节点上实现高能效和低延迟。;在ECG信号处理部分,我们首先提出一种利用从身体传感器网络获得的身体活动上下文信息,可帮助在日常行走环境中检测QRS波,这是ECG信号中最重要的波形。人体活动信息用于选择最佳导联和QRS复杂检测器,以便在具有不同信噪比(SNR)的环境中可以实现最佳QRS复杂检测性能。通过定位QRS复合体,开发了基于隐马尔可夫模型(HMM)的技术来执行更详细的ECG分割。为了使HMM迅速适应时间变化并减少失准误差,提出了一种基于人体传感器网络的主动HMM参数自适应算法。代替单个通用模型,而是使用多个个性化的HMM来改善时间适应性。对ECG信号进行分段后,就可以执行临床测试,例如运动测试(ET)期间的心率恢复(HRR)和ST段压低分析。然后引入基于多元自回归(MAR)的传感器融合技术,以结合网络中来自异构生物传感器的传感数据的优势来提高ECG处理的可靠性和准确性;因为没有令人满意的多生理参数数据库可支持全部人体传感器网络的频谱研究需要建立一个定制的硬件平台来收集生物信号。通过该平台,各种生物传感器可以无线通信,并且可以同时灵活地从异构生物传感器获得生理参数。讨论了为各种生物信号采集设计的硬件平台。介绍了PCB上的主要组件和原理图。

著录项

  • 作者

    Li, Huaming.;

  • 作者单位

    Michigan Technological University.;

  • 授予单位 Michigan Technological University.;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 150 p.
  • 总页数 150
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;
  • 关键词

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