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Electrocardiogram breathing: A study of electrocardiogram derived respiration plus multiresolution wavelet analysis of heartbeat interval variability in sleep study patients.

机译:心电图呼吸:对睡眠研究患者的心电图得出的呼吸以及心律间隔变异性的多分辨率小波分析进行的研究。

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摘要

This dissertation presents a unique research opportunity by using recordings which provide electrocardiogram (ECG) plus a reference breathing signal (RBS). ECG derived breathing (EDR) is measured and correlated against RBS. Standard deviations of multiresolution wavelet analysis coefficients (SDMW) are obtained from heart rate and classified using RBS. Prior works by others used select patients for sleep apnea scoring with EDR but no RBS. Another prior work classified select heart disease patients with SDMW but no RBS. This study used randomly chosen sleep disorder patient recordings; central and obstructive apneas, with and without heart disease.; Implementation required creating an application because existing systems were limited in power and scope. A review survey was created to choose a development environment. The survey is presented as a learning tool and teaching resource. Development objectives were rapid development using limited resources (manpower and money). Open Source resources were used exclusively for implementation.; Results show: (1) Three groups of patients exist in the study. Grouping RBS correlations shows a response with either ECG interval or amplitude variation. A third group exists where neither ECG intervals nor amplitude variation correlate with breathing. (2) Previous work done by other groups analyzed SDMW. Similar results were found in this study but some subjects had higher SDMW, attributed to a large number of apneas, arousals and/or disconnects. SDMW does not need RBS to show apneic conditions exist within ECG recordings. (3) Results in this study support the assertion that autonomic nervous system variation was measured with SDMW. Measurements using RBS are not corrupted due to breathing even though respiration overlaps the same frequency band.; Overall, this work becomes an Open Source resource which can be reused, modified and/or expanded. It might fast track additional research. In the future the system could also be used for public domain data. Prerecorded data exist in similar formats in public databases which could provide additional research opportunities.
机译:本论文通过提供心电图(ECG)和参考呼吸信号(RBS)的记录,提供了独特的研究机会。测量心电图得出的呼吸(EDR)并将其与RBS相关联。从心率获得多分辨率小波分析系数(SDMW)的标准偏差,并使用RBS对其进行分类。其他人的先前工作是使用EDR但未使用RBS对部分患者进行睡眠呼吸暂停评分。另一项先前的工作对SDMW但无RBS的部分心脏病患者进行了分类。这项研究使用了随机选择的睡眠障碍患者录音。中枢性和阻塞性呼吸暂停,有或没有心脏病。实现需要创建一个应用程序,因为现有系统的功能和范围受到限制。创建了审查调查以选择开发环境。此次调查是作为一种学习工具和教学资源而提出的。发展目标是利用有限的资源(人力和金钱)实现快速发展。开源资源仅用于实施。结果表明:(1)研究中存在三组患者。分组RBS相关性会显示ECG间隔或幅度变化的响应。存在第三组,其中ECG间隔和振幅变化均与呼吸无关。 (2)其他小组以前所做的工作分析了SDMW。在这项研究中发现了相似的结果,但是一些受试者的SDMW较高,这归因于大量的呼吸暂停,唤醒和/或断开。 SDMW不需要RBS即可显示ECG记录中存在呼吸暂停状况。 (3)这项研究的结果支持使用SDMW测量自主神经系统变异的说法。即使呼吸重叠在同一频带上,使用RBS进行的测量也不会因呼吸而损坏。总的来说,这项工作成为一个开源资源,可以重复使用,修改和/或扩展。它可能会快速跟踪其他研究。将来,该系统还可用于公共领域的数据。预记录的数据以相似的格式存在于公共数据库中,这可能会提供更多的研究机会。

著录项

  • 作者

    Watson, Herman Lanier.;

  • 作者单位

    Florida International University.;

  • 授予单位 Florida International University.;
  • 学科 Engineering Biomedical.; Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 128 p.
  • 总页数 128
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;无线电电子学、电信技术;
  • 关键词

  • 入库时间 2022-08-17 11:39:51

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