發(fā)布時(shí)間：2018-02-26 03:03

  本文關(guān)鍵詞： 生物雷達(dá) 特征分析 睡眠呼吸暫停綜合癥 模式識別　出處：《第四軍醫(yī)大學(xué)》2012年碩士論文　論文類型：學(xué)位論文

【摘要】：應(yīng)用生物雷達(dá)技術(shù)，可以隔著衣物、被褥等物體對人體的心跳、呼吸等生理運(yùn)動(dòng)進(jìn)行非接觸監(jiān)測，與傳統(tǒng)的接觸式監(jiān)測相比，這一過程會(huì)減少電極、導(dǎo)線、傳感器等對被監(jiān)測目標(biāo)引起的不適感，還可以克服接觸式監(jiān)測方法對大面積燒傷、惡性傳染病等病人監(jiān)測的局限性。因此生物雷達(dá)技術(shù)在臨床上有很廣泛的應(yīng)用前景。 睡眠呼吸暫停綜合癥是一種臨床上常見的疾病，對人類的健康有較大的危害。多導(dǎo)睡眠圖監(jiān)測是臨床診斷睡眠呼吸暫停綜合癥的金標(biāo)準(zhǔn)，在診斷的過程中需要長時(shí)間將很多導(dǎo)線連接到病人的體表，會(huì)給病人帶來很大不便以及身體上的不適感。因此我們提出將生物雷達(dá)技術(shù)應(yīng)用于正常的睡眠呼吸與睡眠呼吸暫停的鑒別，為了實(shí)現(xiàn)這一目標(biāo)，，我們主要完成了以下工作： 1.生物雷達(dá)采集呼吸信號的可靠性研究 建立生物雷達(dá)檢測呼吸信號和綁帶式檢測呼吸信號的同步檢測系統(tǒng)，將生物雷達(dá)采集的呼吸信號與綁帶式壓力傳感器方法采集的呼吸信號進(jìn)行相關(guān)性研究，驗(yàn)證了生物雷達(dá)監(jiān)測呼吸信號的可靠性，為后續(xù)的進(jìn)一步研究奠定基礎(chǔ)。 2.生物雷達(dá)采集的呼吸信號去噪 應(yīng)用生物雷達(dá)對人體長時(shí)間進(jìn)行呼吸監(jiān)測的過程中，會(huì)有一些其它的信號對呼吸信號產(chǎn)生干擾，因此我們采用基于Kaiser窗的低通濾波器和基于等波紋逼近法的低通濾波器對呼吸信號進(jìn)行濾波，提取較為純凈的呼吸信號，并對這兩種方法進(jìn)行了比較。 3.生物雷達(dá)采集的呼吸信號的特征分析 對于生物雷達(dá)采集的正常呼吸信號，我們提取了每個(gè)呼吸周期的最大值和最小值，并計(jì)算固定時(shí)長的呼吸能量。通過計(jì)算最值的間期可以得到呼吸頻率，通過對呼吸能量的計(jì)算可以反映出呼吸運(yùn)動(dòng)的強(qiáng)弱。 4.睡眠呼吸暫停綜合癥的分析鑒別 根據(jù)睡眠呼吸暫停綜合癥的發(fā)病特征模擬睡眠呼吸暫停綜合癥，用生物雷達(dá)采集呼吸信號，采用模式識別的方法對睡眠呼吸暫停與正常睡眠呼吸進(jìn)行鑒別。 本課題的主要?jiǎng)?chuàng)新點(diǎn)： 1.采用等波紋逼近的方法設(shè)計(jì)低通濾波器，對生物雷達(dá)采集的呼吸信號進(jìn)行濾波，相比其他方法的濾波器，實(shí)現(xiàn)相同的效果所需的濾波器階數(shù)更低。 2.對生物雷達(dá)采集的正常呼吸信號和模擬睡眠呼吸暫停時(shí)的呼吸信號進(jìn)行特征值提取，并應(yīng)用短時(shí)平均幅度、短時(shí)方差、短時(shí)頻譜中某一點(diǎn)的頻率分量三個(gè)特征向量對兩種呼吸狀態(tài)進(jìn)行區(qū)分。
[Abstract]:With the use of biological radar technology, non-contact monitoring of physiological movements such as heartbeat, breathing, and so on can be carried out through clothing, bedding and other objects. Compared with traditional contact monitoring, this process will reduce electrodes and conductors. The sensor can overcome the limitation of contact monitoring for patients with large area burn and malignant infectious disease, so it has a wide application prospect in clinic. Sleep apnea syndrome is a common clinical disease that is harmful to human health. Polysomnography monitoring is the gold standard for clinical diagnosis of sleep apnea syndrome. It takes a long time to connect a lot of wires to the patient's surface. It can cause great inconvenience and discomfort to patients. Therefore, we propose to apply the biological radar technology to the identification of normal sleep breathing and sleep apnea. In order to achieve this goal, we have mainly accomplished the following tasks:. 1. Research on the reliability of respiratory signal acquisition by biological radar. A synchronous detection system is established for detecting respiratory signals by biometric radar and bandage. The correlation between the respiratory signals collected by biometric radar and the respiratory signals collected by the method of bandage pressure sensor is studied. The reliability of monitoring respiratory signals by biological radar is verified, which lays a foundation for further research. 2.Respiratory signal denoising from biological radar. Some other signals will interfere with the respiratory signals during the long period of breathing monitoring by using the biometric radar. Therefore, we use low-pass filter based on Kaiser window and low-pass filter based on equal-ripple approximation to filter respiratory signal, extract purer respiratory signal, and compare the two methods. 3. Characteristic analysis of respiratory signals collected by biological radar. For the normal respiratory signals collected by the biological radar, we extracted the maximum and minimum values of each respiration cycle, and calculated the respiration energy of the fixed period. The strength of respiratory movement can be reflected by the calculation of respiratory energy. 4. Analysis and identification of sleep apnea syndrome. According to the characteristics of sleep apnea syndrome, sleep apnea syndrome was simulated. The respiratory signals were collected by biological radar, and the normal sleep apnea and sleep apnea were identified by pattern recognition. The main innovation points of this subject are as follows:. 1. Using the equal-ripple approximation method to design the low-pass filter and filter the respiratory signal collected by the biological radar. Compared with the filter of other methods, the order of the filter needed to achieve the same effect is lower. 2. To extract the characteristic values of the normal respiratory signals and the respiratory signals of simulated sleep apnea, and to apply the short time mean amplitude and short time variance. The frequency components of a point in a short time spectrum are distinguished by three eigenvectors.
【學(xué)位授予單位】：第四軍醫(yī)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R766;R318.0

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

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