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表面膈肌肌电图中神经吸气时间的起始和结束估计:健康受试者的初步研究。

Onset and Offset Estimation of the Neural Inspiratory Time in Surface Diaphragm Electromyography: A Pilot Study in Healthy Subjects.

出版信息

IEEE J Biomed Health Inform. 2018 Jan;22(1):67-76. doi: 10.1109/JBHI.2017.2672800. Epub 2017 Feb 22.

DOI:10.1109/JBHI.2017.2672800
PMID:28237936
Abstract

This study evaluates the onset and offset of neural inspiratory time estimated from surface diaphragm electromyographic (EMGdi) recordings. EMGdi and airflow signals were recorded in ten healthy subjects according to two respiratory protocols based on respiratory rate (RR) increments, from 15 to 40 breaths per minute (bpm), and fractional inspiratory time (T/T) decrements, from 0.54 to 0.18. The analysis of EMGdi signal amplitude is an alternative approach for the quantification of neural respiratory drive. The EMGdi amplitude was estimated using the fixed sample entropy computed over a 250 ms moving window of the EMGdi signal (EMGdi). The neural onset was detected through a dynamic threshold over the EMGdi using the kernel density estimation method, while neural offset was detected by finding when the EMGdi had decreased to 70% of the peak value reached during inspiration. The Bland-Altman analysis between airflow and neural onsets showed a global bias of 46 ms in the RR protocol and 22 ms in the T /T protocol. The Bland-Altman analysis between airflow and neural offsets reveals a global bias of 11 ms in the RR protocol and -2 ms in the T/T protocol. The relationship between pairs of RR values (Pearson's correlation coefficient of 0.99, Bland-=Altman limits of -2.39 to 2.41 bpm, and mean bias of 0.01 bpm) and between pairs of T/T values (Pearson's correlation coefficient of 0.86, Bland-Altman limits of -0.11 to 0.10, and mean bias of -0.01) showed a good agreement. In conclusion, we propose a method for determining neural onset and neural offset based on noninvasive recordings of the electrical activity of the diaphragm that requires no filtering of cardiac muscle interference.

摘要

本研究评估了从表面膈肌肌电图(EMGdi)记录中估计的神经吸气时间的起始和结束。根据基于呼吸频率(RR)增量(从 15 次呼吸/分钟到 40 次呼吸/分钟)和分数吸气时间(T/T)减量(从 0.54 到 0.18)的两种呼吸协议,在 10 位健康受试者中记录 EMGdi 和气流信号。EMGdi 信号的固定样本熵分析是量化神经呼吸驱动的替代方法。使用 EMGdi 信号的 250ms 移动窗口计算的固定样本熵来估计 EMGdi 幅度(EMGdi)。通过使用核密度估计方法对 EMGdi 进行动态阈值检测来检测神经起始,而通过找到 EMGdi 降低到吸气期间达到的峰值的 70%时来检测神经结束。RR 方案中的神经起始与气流之间的 Bland-Altman 分析显示出全局偏差为 46ms,而 T/T 方案中的神经起始与气流之间的 Bland-Altman 分析显示出全局偏差为 22ms。RR 方案中的神经结束与气流之间的 Bland-Altman 分析显示出全局偏差为 11ms,而 T/T 方案中的神经结束与气流之间的 Bland-Altman 分析显示出全局偏差为-2ms。RR 值对(Pearson 相关系数为 0.99,Bland-Altman 限制为-2.39 至 2.41 bpm,平均偏差为 0.01 bpm)和 T/T 值对(Pearson 相关系数为 0.86,Bland-Altman 限制为-0.11 至 0.10,平均偏差为-0.01)之间的关系显示出良好的一致性。总之,我们提出了一种基于膈肌电活动的无创记录来确定神经起始和神经结束的方法,该方法不需要滤除心肌干扰。

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