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Acquisition of cardiac control parameters from whole vagus nerve recordings.

机译:从整个迷走神经记录中获取心脏控制参数。

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摘要

Heart rate varies continuously depending on the amount of activity being performed or the emotional state of an individual. Both branches of the autonomic nervous system work to alter heart rate depending on the needs of the body. While healthy individuals are capable of altering their heart rate, individuals with certain types of heart disease do not have this ability. For these individuals, cardiac pacemakers are used to alter heart rate. Cardiac pacemakers use sensors to determine the pacing frequency for the heart; however, there is no current optimum sensor. In order to discover a better sensor, this study investigated the use of parasympathetic motor activity via the vagus nerve to predict heart rate.; Vagus nerve activity and EKG signals were recorded simultaneously; two types of recordings were taken: baseline and altered heart rate recordings achieved by performing bi-lateral carotid artery occlusion. Whole vagus nerve discharges were recorded using small silicone cuff electrodes with platinum contacts. Neural activity and EKG signals obtained from these experiments were filtered for frequency content. After filtering, the vagus motor signal was calculated by using a cross correlation technique introduced by Heetderks. The vagus motor activity was integrated between successive R waves taken from the recorded EKG and correlated with instantaneous heart rate. Consistent, high inverse correlations between integrated vagus motor activity and instantaneous heart rate were found in baseline and occlusion recordings. After obtaining consistent correlations between the integrated vagal motor activity and instantaneous heart rate, a transfer function model was developed using time series analysis methods. The transfer function model whose input was integrated vagus motor activity and whose output was heart rate was capable of predicting heart rate within a 95% confidence interval.
机译:心律持续变化,取决于进行的活动量或个人的情绪状态。自主神经系统的两个分支都根据身体的需要来改变心率。尽管健康的人有能力改变他们的心律,但是患有某些类型的心脏病的人却没有这种能力。对于这些人,使用心脏起搏器来改变心率。心脏起搏器使用传感器确定心脏的起搏频率。但是,目前没有最佳传感器。为了发现更好的传感器,本研究调查了通过迷走神经使用副交感神经运动来预测心律。同时记录迷走神经活动和EKG信号;记录有两种类型:基线和通过执行双侧颈总动脉闭塞而改变的心率记录。使用带有铂触点的小型硅胶袖带电极记录整个迷走神经放电。从这些实验中获得的神经活动和心电图信号被滤除频率成分。滤波后,通过使用Heetderks引入的互相关技术来计算迷走运动信号。迷走运动活动在记录的心电图连续的R波之间进行积分,并与瞬时心率相关。在基线和咬合记录中发现迷走运动综合活动与瞬时心率之间存在一致的高度反相关。在获得迷走运动综合活动量和瞬时心率之间的一致关系后,使用时间序列分析方法建立了传递函数模型。输入为迷走运动活动积分且输出为心率的传递函数模型能够在95%的置信区间内预测心率。

著录项

  • 作者

    Pool, Marcia A.;

  • 作者单位

    Louisiana Tech University.;

  • 授予单位 Louisiana Tech University.;
  • 学科 Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2008
  • 页码 142 p.
  • 总页数 142
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;
  • 关键词

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