本文關(guān)鍵詞：基于脈搏信號的心電干擾段心率變異性估計(jì)　出處：《蘭州理工大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 干擾段 短時(shí)自相關(guān) 心率變異性 脈搏信號 滑窗迭代DFT

【摘要】：心率變異性(Heart Rate Variability,HRV)產(chǎn)生于心臟搏動(dòng)間期的周期性變化,是反映自主神經(jīng)系統(tǒng)交感神經(jīng)與迷走神經(jīng)活性及其平衡性的重要指標(biāo),可用于許多心血管疾病和非心血管疾病的預(yù)測和診斷。通常,HRV從心電信號中獲取,但是獲取心電信號的電極導(dǎo)聯(lián)和連線繁雜,同時(shí),心電信號監(jiān)護(hù)設(shè)備所采集的信號經(jīng)常含有人體運(yùn)動(dòng)等因素引起的干擾段,干擾段的HRV難以直接提取,從而導(dǎo)致一些以HRV為基礎(chǔ)的疾病檢測方法或系統(tǒng)產(chǎn)生誤判,可靠性降低。因此,為了提高心電信號干擾段檢測的準(zhǔn)確性和實(shí)時(shí)性,且準(zhǔn)確估計(jì)干擾段信號的HRV,本文提出了一種新的心電干擾段HRV估計(jì)方法。該方法采用短時(shí)自相關(guān)原理檢測心電信號中的干擾段,并利用脈搏信號與心電信號間的相關(guān)性,采用基于自相關(guān)函數(shù)的快速功率譜估計(jì)法和改進(jìn)的滑窗迭代DFT算法,分別從同步采集的脈搏信號中估計(jì)心電干擾段的HRV,并與現(xiàn)有的典型算法進(jìn)行比較。結(jié)果表明,文中提出的HRV估計(jì)算法具有較高的準(zhǔn)確性和較強(qiáng)的實(shí)時(shí)性。本文主要研究了以下四個(gè)方面:(1)心電脈搏信號的濾波。心電脈搏信號都為微弱的信號,在它們的采集的過程中會(huì)引入各類噪聲和干擾。通常采集的心電脈搏信號中都會(huì)含有基漂、肌電干擾和50Hz的工頻干擾。為了濾除這些干擾和噪聲信號,本文首先對標(biāo)準(zhǔn)的心電脈搏信號波形進(jìn)行了介紹,然后設(shè)計(jì)實(shí)時(shí)性比IIR和FIR濾波器更強(qiáng)的整系數(shù)濾波器進(jìn)行濾波。(2)心電信號干擾段檢測。根據(jù)心電信號干擾段的特點(diǎn),采用短時(shí)自相關(guān)原理快速檢測心電信號的干擾段,并結(jié)合光滑度和動(dòng)態(tài)變異系數(shù)兩個(gè)動(dòng)態(tài)參數(shù)提高干擾段檢測的準(zhǔn)確度。采用國際常用MIT-BIT Arrhythmia Database/Challenge2014 Training Set(challenge/2014/set-p)數(shù)據(jù),對提出的干擾段檢測算法和現(xiàn)有常用的幾種干擾段檢測算法進(jìn)行仿真比較,評估所提算法的性能。結(jié)果表明文中提出的干擾段檢測算法的準(zhǔn)確度更高并且實(shí)時(shí)性更強(qiáng)。(3)心電干擾段的HRV估計(jì)。利用心電信號和脈搏信號之間的相關(guān)性,采用基于自相關(guān)函數(shù)的快速功率譜估計(jì)法和改進(jìn)的滑窗迭代DFT算法,分別從同步采集的脈搏信號中估計(jì)心電干擾段的HRV。并將文中提出的HRV估計(jì)算法與目前常用的HRV估計(jì)算法進(jìn)行仿真比較,評估文中算法的有效性和可靠性。(4)基于心率變異性估計(jì)的冠心病識別方法研究�？紤]到算法的實(shí)時(shí)性和準(zhǔn)確性,采用文中提出的改進(jìn)滑窗迭代DFT算法提取健康人和冠心病患者的HRV,針對它們的不同,分別提取它們時(shí)域和頻域特征,然后進(jìn)行特征選擇,選取最佳的特征組合,采用優(yōu)化的支持向量機(jī)對選取的特征組合分類。最后,與常用的幾種智能算法進(jìn)行實(shí)驗(yàn)比較,分析各種算法的優(yōu)缺點(diǎn),對文中的分類算法進(jìn)行進(jìn)一步性能評估。
[Abstract]:Heart rate variability (HRV) is produced by periodic changes in heart beat interval. It is an important indicator of sympathetic and vagal nerve activity and its balance in autonomic nervous system. It can be used in the prediction and diagnosis of many cardiovascular diseases and non-cardiovascular diseases. At the same time, the ECG signal collected by ECG monitoring equipment often contains interference segment caused by human movement and other factors, the HRV of interference segment is difficult to be directly extracted. Therefore, in order to improve the accuracy and real time of ECG interference detection, some disease detection methods or systems based on HRV cause misjudgment and lower reliability. In this paper, a new HRV estimation method for ECG interference segment is proposed, which uses the principle of short time autocorrelation to detect the interference segment in ECG signal. Using the correlation between pulse signal and ECG signal, the fast power spectrum estimation method based on autocorrelation function and the improved sliding window iterative DFT algorithm are adopted. The HRV of ECG interference segment is estimated from synchronously collected pulse signals, and compared with the existing typical algorithms. The results show that. The HRV estimation algorithm proposed in this paper has higher accuracy and better real-time. This paper mainly studies the filtering of ECG pulse signal in the following four aspects: 1) ECG pulse signal are all weak signals. All kinds of noise and interference will be introduced in the process of their acquisition. The ECG pulse signals usually collected will contain fundamental drift, electromyography interference and 50 Hz power frequency interference. In order to filter out these interference and noise signals. In this paper, the standard waveform of ECG pulse signal is introduced. Then, an integer coefficient filter, which is stronger than IIR and FIR filters in real time, is designed to detect the interference segment of ECG signal. According to the characteristics of the interference segment of ECG signal. The short time autocorrelation principle is used to detect the interference segment of ECG signal. The accuracy of interference detection was improved by combining the two dynamic parameters of smoothness and dynamic coefficient of variation. MIT-BIT Arrhythmia was adopted in this paper. Database/Challenge2014 Training Settings (. Challenging / 2014 / set-p data. The proposed jamming segment detection algorithm and several commonly used jamming segment detection algorithms are simulated and compared. The performance of the proposed algorithm is evaluated. The results show that the proposed interference segment detection algorithm is more accurate and real-time. HRV estimation of ECG interference segment. Correlation between ECG and pulse signals was used. The fast power spectrum estimation method based on autocorrelation function and the improved sliding window iterative DFT algorithm are adopted. HRVs of ECG interference segment are estimated from synchronously collected pulse signals, and the HRV estimation algorithm proposed in this paper is simulated and compared with the HRV estimation algorithm commonly used at present. Evaluation of the effectiveness and reliability of the algorithm in this paper. (4) Research on the recognition method of coronary heart disease based on heart rate variability estimation, taking into account the real-time and accuracy of the algorithm. The improved sliding window iterative DFT algorithm proposed in this paper is used to extract the HRVs of healthy persons and patients with coronary heart disease (CHD). According to their differences, the time-domain and frequency-domain features are extracted, and then the feature selection is carried out. The best feature combination is selected, and the selected feature combination is classified by the optimized support vector machine. Finally, the advantages and disadvantages of each algorithm are analyzed and compared with several common intelligent algorithms. Further performance evaluation of the classification algorithm is carried out.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：R540.4

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