冠心病病人脉搏变异性信号的去趋势波动分析

Detrended Fluctuation Analysis of Pulse Rate Variability Signals of Coronary Disease Patients.

目的本文运用非线性方法分析冠心病人与健康人的脉搏变异性信号,研究两者之间不同的特征,以便无创地检测冠心病及监测冠心病人的病情。方法利用PPG传感器采集健康人(18人)和冠心病患者(18人)的指尖脉搏波信号,并从脉搏波中提取脉搏变异性信号,利用去趋势波动分析算法对其进行分析,计算两类病人各自的标度指数。结果经过去趋势波动算法的分析,发现冠心病病人的平均标度指数:短程α1、长程α2和全程α明显低于健康人。结论运用去趋势波动算法分析健康人和冠心病患者的脉搏变异性信号,发现冠心病病人的平均标度指数α1、α2和α明显有别于健康人,这些现象的发现有助于对冠心病及冠心病人的病情进行无创检测和监测。

Objective Nonlinear method was applied to the analysis of pulse rate variability （PRV） signal of healthy people and coronary disease patients to find the different features, which can be used to detect coronary disease invasively and monitor the process of the disease. Methods Pulse waves of the healthy people and patients （ 18 subjects in each group） were sampled by photoplethysmographic （PPG） sensor. The detrended fluctuation analysis algorithm was applied to PRV series which extracted from PPG signals, and the scaling exponents of the two groups of signals were obtained. Results The average scaling exponents of the patients, i.e. short term α1, long term α2 and whole process α, were significantly lower than those of the healthy subjects. Conclusion Detrended fluctuation analysis algorithm was applied to the PRV signal of the healthy subjects and coronary disease patients. It was found that the average scaling exponents of the patients, α1,α2 and α, are significantly different from those of the healthy people. These findings suggest that nonlinear index could be used to detect and monitor coronary disease non-invasively.