【作者】 孙松松；

【导师】 张爱华；

【作者基本信息】 兰州理工大学 ， 信号与信息处理， 2012， 硕士

【摘要】 随着人类社会的不断发展进步,人们的生活水平也逐步提高,同时生活节奏也越来越快，促使人们对生命健康质量有了更高的要求，然而长时间从事脑力劳动、睡眠不足、情绪压抑、过度疲劳会引发内脏功能失调、心血管功能紊乱、腺体分泌异常，这些都影响着人们的正常生活。现代医学通过生理信号的变化断对病人身体状进行评估，然而单一信号(心电、脉搏等)对人体的生理状态的变化判别能力较差。本文从两种生理信号同步分析角度出发，通过探讨人体生理信号之间的内在联系，提出了基于心电和脉搏信息融合分析的研究方法。主要内容如下：（1）通过同步采集平台对心电信号和脉搏信号进行采集，对同步采集的信号做处理，得到疲劳实验前后的心电信号和脉搏信号。（2）选择合适的滤波器对心电信号和脉搏信号进行预处理，观察时域和频域上基线漂移、工频干扰、肌电干扰的滤除状况，得到准确的波形。（3）分析实验前后心电的R波，T波以及脉搏的主波和重搏波前波等特征，利用t检验检测各个特征信息变化的显著性水平，其显著性水平p<0.01；通过R波和脉搏主波间期分析心率变异性(HRV)和脉率变异性(PRV)，对HRV和PRV的间期均值、SDDN、甚低频（VLF）、低频（LF）、高频（HF）、相对频谱分布（LF/HF）和相关系数（ρ）等特征在同一实验状态和不同实验状态下分析。利用t检验各个特征发现有统计学意义p<0.05。最后，通过提取到的心电和脉搏的重要特征，对人体的生理特性进行分析，发现其心率变异性和脉率变异性的相关特性，以及二者和植物性神经之间的关系。并对比不同的信息融合方法，利用特征级融合对心电信号和脉搏信号的视觉疲劳状态进行分析，利用支持向量机(SVM)法对34例样本(正常17例，视觉疲劳17例)的心电和脉搏融合特征进行分类，达到了较高的分类效果。研究结果表明，心电信号和脉搏信号在人体中有极其紧密的相关特征，在利用心电、脉搏信号反映人体疲劳状态的同时；利用心电和脉搏的相关性对疲劳状态的研究和分析是能够达到更好的效果。更多还原

【Abstract】 With the ceaseless development of human society, people’s living standardsgradually improved and spurred to greater demands on the quality of life and health.However, engaging in mental work for a long time, lack of sleep or emotionaldistress, fatigue and irregular life can cause internal function disorder,cardiovascular disorders and unusual glandular secretion. This could affect thepeople’s normal life seriously. Physiological signal is used to determine the healthstatus in the modern medicine. However, the single signal (ECG, pulse, etc.) waspoor to discriminate the body’s physiological state changes. Proceeding fromsimultaneous analysis of two kinds of physiological signals and exploring thecorrelation between human physiological signals, this paper presents a fusionanalysis method based on ECG and pulse signals. The main works were shown asfollowing:(1) The ECG and pulse signals were acquired synchronously before and after thefatigue experiment through the data acquisition system.(2) Preprocess ECG and pulse signal with the appropriate filter, and eliminate thebaseline drift with the frequency50Hz and EMG interference. Then we can obtainaccurate waveforms.(3) The characteristics of the R-wave, T wave, the peak value and the tidal wavepeak of the pulse signals were analyzed. The t test was used to evaluate thesignificance level of each characteristic changes and the significance level are allp<0.01. The R-R intervals and P-P intervals could be extracted from the ECG andthe pulse signals. Analysis the change before and after the visual fatigue experimentin the time domain and frequency domain feature extraction. The mean interval ofHRV and PRV characteristics, SDDN, very low frequency (VLF), low frequency(LF), high frequency (HF), the relative spectral distribution (LF/HF) and correlationwere analyzed under the same experimental state and the different experimentalstates respectively, The t test results have demonstrated each feature has statisticalsignificance p<0.01.Finally, the characteristics of the ECG and pulse were used to analyze thehuman physiological property. The results have shown that there is correlationproperty between HRV/PRV and Autonomic Nerve in VDT Fatigue. Comparingdifferent information fusion methods and using feature fusion to analyze the VDT Fatigue, the accuracy rate of classification reaches up to100%by using SupportVector Machine for the combination features of ECG and pulse wave signals, whichsurpassed the accuracy rate classified by one kind of biomedical signal.The results have shown that ECG and pulse signal in the human body are veryclosely related characteristics. Not only the ECG and the pulse signals can reflect thefatigue state, but also the correlation between ECG and pulse signals have bettereffect on the research and analysis of the fatigue state.更多还原