本文關(guān)鍵詞： 心理壓力 心率變異性 序列后向選擇 人工神經(jīng)網(wǎng)絡(luò) 支持向量機(jī)　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：現(xiàn)代社會(huì)中,越來越多的人受到心理壓力的困擾,不同程度的心理壓力會(huì)對人產(chǎn)生生理和心理上的影響。此外,長時(shí)間的心理壓力還可能引發(fā)抑郁,從身體和心理上給人帶來極大的痛苦和折磨。因此,如何能夠準(zhǔn)確評估人們所處的壓力程度,從而采取有效的調(diào)節(jié)手段是近幾年研究的熱點(diǎn)。目前對于壓力的評估主要分為基于心理問卷的壓力評估和基于生理參數(shù)的壓力評估兩大類,前者雖然更容易量化且便于統(tǒng)計(jì)分析,但是往往需要參與者積極的響應(yīng)和配合,更容易受到個(gè)體主觀的影響,這種方法得到的壓力識(shí)別結(jié)果往往被認(rèn)為是不可靠的。當(dāng)人體受到心理壓力刺激時(shí),人體的生理平衡狀態(tài)將會(huì)發(fā)生變化,這會(huì)直接導(dǎo)致人體各項(xiàng)生理參數(shù)的改變。因此,本文提出采用心率變異性信號對于人體所處的壓力狀態(tài)進(jìn)行識(shí)別和評估,并在此基礎(chǔ)上,對于能夠反映心臟自主神經(jīng)活動(dòng)的心率變異性指標(biāo)進(jìn)行分析,尋找其隨著壓力程度的增大而表現(xiàn)出的變化規(guī)律,從而可方便的實(shí)現(xiàn)對心理壓力程度的監(jiān)測,更好的實(shí)現(xiàn)心理壓力的識(shí)別與評估。本文的主要研究內(nèi)容如下:(1)參考心算任務(wù)范式,設(shè)計(jì)誘發(fā)心理壓力的實(shí)驗(yàn)方案,并對實(shí)驗(yàn)得到的量表和任務(wù)績效兩項(xiàng)指標(biāo)進(jìn)行分析,將實(shí)驗(yàn)誘發(fā)的心理壓力分為三個(gè)程度。(2)對收集到的原始ECG信號進(jìn)行預(yù)處理。在本文中,采用小波變換的方法去除了ECG信號的噪聲干擾。對濾除噪聲后的信號經(jīng)過R波定位,提取出HRV信號。從時(shí)域、頻域以及非線性三個(gè)方面提取HRV特征共計(jì)26維,并在此基礎(chǔ)上對特征進(jìn)行預(yù)處理。(3)建立心理壓力程度三分類模型。采用SBS算法,構(gòu)建符合實(shí)驗(yàn)要求的BP網(wǎng)絡(luò)模型和SVM分類器,從提取的原始特征中選擇出對分類貢獻(xiàn)度最高的特征集,并對兩種分類器的分類準(zhǔn)確率通過留一驗(yàn)證法進(jìn)行評估發(fā)現(xiàn)兩種分類器的識(shí)別率均在80%以上。(4)通過重復(fù)測量方差分析對第四章選取的不依賴于本文構(gòu)建的兩種分類器的特征集中的每個(gè)特征進(jìn)行分析,判斷在不同心理壓力程度下每個(gè)特征的顯著性差異水平。找出隨著壓力程度的變化,特征的變化規(guī)律,并對其造成的神經(jīng)活動(dòng)機(jī)制進(jìn)行分析。本文經(jīng)過深入研究發(fā)現(xiàn),參考心算任務(wù)的實(shí)驗(yàn)范式,通過時(shí)間來控制心算任務(wù)難度能夠誘發(fā)出不同程度的心理壓力,采用SBS算法,并構(gòu)建符合實(shí)驗(yàn)要求的BP網(wǎng)絡(luò)模型和SVM兩種分類器,將SBS與兩種分類器分別結(jié)合得到的兩種分類器的平均識(shí)別率均在80%以上,并選擇出對兩種分類器貢獻(xiàn)度最高的特征組合。說明心臟自主神經(jīng)活動(dòng)可以在一定程度上區(qū)分心理壓力。隨著壓力程度的增大,HRV指標(biāo)中的SDNN顯著減低而aHF顯著升高,由此可以推測出隨著壓力程度的升高,心臟交感-迷走神經(jīng)活動(dòng)產(chǎn)生了改變,交感神經(jīng)功能在一定程度上受到抑制而迷走神經(jīng)功能則相對興奮。我們可以參考這幾項(xiàng)指標(biāo)的變化規(guī)律對壓力程度進(jìn)行檢測和評估。
[Abstract]:In modern society, more and more people are troubled by psychological pressure, different degrees of psychological pressure will have a physiological and psychological impact on people. In addition, long-term psychological stress may also lead to depression. Physical and psychological pain and suffering. Therefore, how to accurately assess the degree of stress people are in. In recent years, stress assessment is mainly divided into two categories: psychological questionnaire based stress assessment and physiological parameter based stress assessment. Although the former is easier to quantify and easy to analyze, it often needs the active response and cooperation of the participants and is more susceptible to the subjective influence of the individual. The results of this method are often regarded as unreliable. When the body is stimulated by psychological stress, the physiological balance of the body will change. This will directly lead to the changes of human physiological parameters. Therefore, this paper proposes the use of heart rate variability signal to identify and evaluate the pressure state of the human body, and on this basis. The indexes of heart rate variability which can reflect cardiac autonomic nervous activity are analyzed to find out the change law of heart rate variability with the increase of stress degree, so that the monitoring of psychological stress degree can be realized conveniently. The main contents of this paper are as follows: 1) referring to the task paradigm of mental arithmetic, the experimental scheme of inducing psychological stress is designed. The scale and task performance were analyzed and the psychological stress induced by the experiment was divided into three levels. (2) the original ECG signal was preprocessed. In this paper. The noise interference of ECG signal is removed by wavelet transform, and the HRV signal is extracted from time domain by R-wave localization of filtered noise signal. On the basis of extracting 26 dimensional HRV features from frequency domain and nonlinear three aspects and preprocessing the features, a three classification model of degree of psychological stress is established. SBS algorithm is adopted. The BP neural network model and the SVM classifier which meet the requirements of the experiment are constructed, and the feature sets with the highest contribution to the classification are selected from the original features extracted. The classification accuracy of the two classifiers was evaluated by a residual verification method. It was found that the recognition rates of the two classifiers were above 80%. Through repeated measurement variance analysis, we analyze each feature in the feature set of the two classifiers which are not dependent on the two classifiers constructed in this paper in Chapter 4th. Judge the significant difference level of each characteristic under different degree of psychological stress. Find out the change rule of characteristic with the change of stress degree. After in-depth study, this paper found that referring to the experimental paradigm of mental arithmetic task, the difficulty of mental arithmetic task can be controlled by time to induce different degrees of psychological stress. Adopting SBS algorithm and constructing BP neural network model and SVM classifier, the average recognition rate of the two classifiers combined with SBS and the two classifiers are above 80%. And selected to the two types of classifier contribution to the highest combination of characteristics, indicating that cardiac autonomic nervous activities can to a certain extent distinguish psychological stress, with the increase of stress. SDNN in the HRV index was significantly decreased and aHF was significantly increased. It can be inferred that cardiac sympathetic vagal nerve activity changes with the increase of the stress level. The sympathetic function is inhibited to some extent while the vagal function is relatively excited.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R318;TN911.7

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