基于经验模态分解的RR间期频域成分分析方法

张未英; 杨东变; 姚洁

doi:10.12125/j.chj.201906052

基于经验模态分解的RR间期频域成分分析方法

Analysis of R-R interval spectrum power based on empirical mode decompositions

摘要

摘要:
目的自主神经能够调控心血管系统，对高血压、糖尿病等慢病的发生、发展有重要的影响，对其功能的评测有重要价值。RR间期的频域值常被用来表征自主神经功能，但近年来研究表明该指标并不能很好量化交感和副交感活性，本文提出了一种基于呼吸的改进自主神经功能评测方法。
方法通过经验模态分析方法将RR序列分解为呼吸相关与呼吸不相关两部分，设计深呼吸与速算两种任务检验传统心率变异性频域成分与本文改进的成分之间的差异。
结果呼吸幅度最大斜率序列与RR间期序列显著相似，基于最大斜率与RR间期的固有模态函数的相关性成功地将心率变异性分解为呼吸相关与呼吸无关两部分，研究表明这两部分在深呼吸与速算过程中分别能够较好的反应任务的自主功能变化。
结论呼吸相关与不相关的RR间期频域成分能够分别表示副交感神经与交感神经活性。

Abstract:
AIM To present a method to well evaluate autonomic nerve system (ANS) activity based on respiration.
METHODS R-R intervals were divided into respiration-related (HFr) components and respiration-unrelated (LFu) components with empirical mode decompositions (EMD). They were used to evaluate the ANS activity in slowly deep breath (SDB) and mental arithmetic test (MAT).
RESULTS Power spectrum components of RR interval were successfully divided into HFr parts and LFu parts based on the relationship between intrinsic mode function (IMF) and maximum respiratory slope. The HFr and LFu power spectrums well evaluated the ANS activity in SDB and MAT, compared with the heart rate variability (HRV) power spectrum.
CONCLUSION The respiration-related and respiration-unrelated power spectrums could be used to evaluate sympathetic and parasympathetic activities.

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