摘要
表征极短时(<1min)心率变异性(HRV)的分析参数通常随时间呈现出复杂的变化模式,这种随时间变化的波动很容易干扰对心血管状态的正确判断。本文将年龄匹配的41例健康人(对照组)和25例充血性心力衰竭(CHF)患者(实验组)的长时HRV序列划分成多段极短时序列,计算同一HRV参数在多段极短时序列中的变异系数以及多次组间t检验中差异有统计学意义的检验比例,以此探讨部分极短时HRV分析参数在揭示不同状态下心血管系统功能差异时的稳定性;此外,通过对受试者工作特征(ROC)曲线的分析以及人工神经网络的建模,评估了这些参数对对照组和实验组进行分类的效果。本文结果表明:1基于复杂网络分析的度分布熵指标有着最小的变异系数且对病理状态敏感(79.75%情况下对照组和实验组的差异有统计学意义),可为临床医生提供一个诊断CHF患者的辅助指标;2将庞加莱散点图进行椭圆拟合后,对照组和实验组的椭圆短长轴之比(SDratio)在98.5%的情况下差异有统计学意义;在人工神经网络建模时,仅使用SDratio对对照组和实验组进行分类的正确率为71.87%,表明SDratio或可作为CHF患者的智能诊断指标;3仍需寻找可用于极短时HRV分析研究且对CHF患者更加敏感特异的稳定指标。
The analysis parameters for the characterization of heart rate variability （HRV） within a very short time （〈1 min） usually exhibit complicate variation patterns over time, which may easily interfere the judgment to the status of the cardiovascular system. In this study, long-term HRV sequence of 41 cases of healthy people （control group） and 25 cases of congestive heart failure （CHF） patients （experimental group） was divided into multiple segments of very short time series. The variation coefficient of the same HRV parameter under multiple segments of very short time series and the testing proportion with statistically significant differences under multiple interclass t-test were calculated. On this account, part of HRV analysis parameters under very short time were discussed to reveal the stability of difference of the cardiovascular system function under different status. Furthermore, with analyzing the receiver operating characteristic （ROC） curve and modeling the artificial neural network （ANN）, the classification effects of these parameters between the control group and the experimental group were assessed. The results demonstrated that （1）the indices of entropy of degree distribution based on the complex network analysis had a lowest variation coefficient and was sensitive to the pathological status （in 79.75 % cases, there has statistically significant differences between the control group and experimental group）, which can be served as an auxiliary index for clinical doctor to diagnose for CHF patient;（2）after conducting ellipse fitting to Poincare plot, in 98.5% cases, there had statistically significant differences for the ratio of ellipse short-long axis （SDratio） between the control group and the experimental group; when modeling the ANN and solely adopting SDratio, the classification accuracy to the control group and experimental group was 71.87%, which demonstrated that SDratio might be acted as the intelligent diagnosis index for CHF patients; （3） however, more sensitive and robust indices were still needed to find out for the very-short HRV analysis and for the diagnosis of CHF patients as well.
出处
《生物医学工程学杂志》
EI
CAS
CSCD
北大核心
2016年第3期559-563,569,共6页
Journal of Biomedical Engineering
基金
国家自然科学基金资助项目(61401518)
江苏省自然科学基金资助项目(BK20141432)
中国药科大学中央高校基本科研业务费专项资金资助项目
关键词
心率变异性
度分布熵
充血性心力衰竭
heart rate variability （HRV）
entropy of the degree distribution （EDD）
congestive heart failure （CHF）
作者简介
通信作者。E—mail：houfz@cpu．edu．cn
