本文選題：心室纖顫 + 持續(xù)性心動過速　； 參考：《濟南大學(xué)》2017年碩士論文

【摘要】：心血管疾病是威脅人類健康的疾病之一。近年來,心血管疾病的發(fā)病率逐漸上升,嚴重危及人們的生命安全。心血管疾病患者越來越多,越來越趨于年輕化。心血管疾病最嚴重的臨床表現(xiàn)就是心臟猝死,如果患者心臟猝死后不能得到及時有效的治療,將會失去生命。正因如此,多國醫(yī)藥衛(wèi)生部門、研究中心都對此進行研究。經(jīng)研究發(fā)現(xiàn),大多數(shù)心臟猝死都是由心室纖顫(Ventricular Fibrillation,VF)或者持續(xù)性心動過速(Ventricular Tachycardia,VT)惡化導(dǎo)致的。針對VF與VT,臨床醫(yī)治方案是不同的。如果患者心室纖顫發(fā)作,必須馬上進行除顫,這是目前效果最佳的治療方案。如果患者是持續(xù)性室性心動過速,需要及時正確的進行藥物治療,以達到降低轉(zhuǎn)為心室纖顫或者心臟猝死發(fā)生率的目的。如果VT被診斷為VF,患者將遭受不必要的電擊,對心臟造成創(chuàng)傷。如果VF被診斷為VT,沒有及時進行除顫,患者將會發(fā)生心臟猝死。目前檢測算法能很好的區(qū)分竇性心律和VT、VF,但是VT與VF的檢測還在持續(xù)研究中。根據(jù)目前對心電信號的研究,可將ECG視為非線性時間序列的范疇。用非線性動力學(xué)理論來分析ECG具有明顯的優(yōu)勢。本文以非線性動力學(xué)為理論依據(jù)對符號時間序列分析方法進行了分析研究,并結(jié)合時間序列分解算法提出了基于序列分解和符號時間序列分析方法的VT/VF檢測新算法,其中包括基于EMD與符號時間序列分析方法的新算法和基于小波分析與符號時間序列分析方法的新算法。然后利用數(shù)據(jù)集對提出的算法進行實驗驗證,結(jié)合符號時間序列理論對實驗結(jié)果進行分析與比較。實驗表明EMD結(jié)合符號時間序列分析方法其VT/VF的識別率為97.82%,小波分析結(jié)合符號時間序列分析方法其VT/VF的識別率為99.5%。并針對時間序列分析方法中的二值粗�；蛔銓ζ涓倪M。通過實驗對比了改進之后算法與改進之前的變化。針對VT/VF的識別度以及算法執(zhí)行時間對改進算法和未改進算法做了對比試驗。實驗表明,在VT/VF識別度基本一致的情況下,改進算法相比未改進算法相比,計算量減少,改進算法的時間縮減了30多倍。而且針對小樣本時,改進算法會獲得更高的VT/VF識別率。換言之,在實時性要求較高的監(jiān)護系統(tǒng)中,改進算法能對輸入信號更快的做出響應(yīng)。對算法進行改進后,計算量大大減小,算法執(zhí)行時間縮短,適合用于監(jiān)護儀、自動體外除顫儀(AED)植和入式除顫儀(ICD)等實時監(jiān)測預(yù)警和自動除顫設(shè)備。最后從樣本時間與符號化等級兩方面對改進算法與單獨的符號時間序列分析方法進行了對比實驗,實驗表明改進算法無論是在樣本時間還是符號化等級相比單獨使用符號時間序列分析方法具有更好的識別性能,說明了基于序列分解算法及符號時間序列分析算法的融合算法的有效性。本文主要創(chuàng)新點:第一,提出了基于經(jīng)驗?zāi)B(tài)分解序列分解方法與符號時間序列分析方法的VT/VF檢測新算法,研究表明該方法對VT/VF具有較高的識別度。第二,提出了基于小波分析與符號時間序列分析方法的VT/VF檢測新算法,研究表明適當(dāng)?shù)男盘柌蓸勇视欣诜枙r間序列分析方法挖掘序列的本質(zhì)特征,通過小波分解得到的分解序列提取符號熵對VT/VF相比EMD具有更高的識別度。最后,分析了符號時間序列分析方法現(xiàn)有的不足,對過粗�；瘑栴}進行了改進,并從多方面對改進的算法進行了分析,實驗表明,改進算法在保持對VT/VF識別率基本不變的情況下其計算量降低,并且針對小樣本具有更高的識別率。
[Abstract]:Cardiovascular disease is one of the diseases that threaten human health. In recent years, the incidence of cardiovascular disease is increasing, which seriously endangers the life safety of people. More and more patients with cardiovascular diseases are becoming younger. The most serious clinical manifestation of cardiovascular disease is the sudden cardiac death, if the sudden death of the heart can not be obtained in time. Effective treatment will lose life. Because of this, many national medical departments, research centers have studied it. Most sudden cardiac deaths have been found to be caused by the deterioration of Ventricular Fibrillation (VF) or Ventricular Tachycardia (VT). For VF and VT, a clinical treatment plan. If the patient is persistent ventricular tachycardia, the patient needs to be treated in time and correctly in order to reduce the rate of ventricular fibrillation or sudden cardiac death. If VT is diagnosed as VF, the patient will suffer. If the VF is diagnosed as VT and does not defibrillate in time, the patient will have a sudden cardiac death. The current detection algorithm can distinguish the sinus rhythm and VT, VF, but the detection of VT and VF is still in a continuous study. According to the current study of ECG signals, ECG can be considered as a nonlinear time series. In this paper, the analysis of ECG has obvious advantages by using nonlinear dynamics theory. In this paper, the method of symbolic time series analysis is analyzed based on nonlinear dynamics, and a new VT/VF detection algorithm based on sequence decomposition and symbol time series analysis is proposed, which includes the time series decomposition algorithm. A new algorithm based on EMD and symbolic time series analysis method and a new algorithm based on wavelet analysis and symbolic time series analysis. The experimental results are analyzed and compared with the symbolic time series theory. The experimental results show that EMD combined with symbolic time series analysis method. The recognition rate of VT/VF is 97.82%. The recognition rate of VT/VF is 99.5%. with the method of wavelet analysis and symbolic time series analysis. The improvement of the two value coarse graining in the time series analysis method is improved. The changes of the algorithm and the improvement before the improved algorithm are compared. The recognition degree of the VT/ VF and the time of the algorithm execution are modified. Compared with the unimproved algorithm, the experiment shows that, when the VT/VF recognition degree is basically consistent, the improved algorithm is less computational than that of the unimproved algorithm, and the time of the improved algorithm is reduced by 30 times. Moreover, the improved algorithm will get a higher VT/VF recognition rate for small samples. In other words, the real-time requirement is better than that of the improved algorithm. In the high monitoring system, the improved algorithm can respond to the input signal faster. After improving the algorithm, the calculation amount is greatly reduced and the execution time of the algorithm is shortened. It is suitable for monitor, automatic defibrillator (AED) plant and ICD defibrillator (ICD) and other real-time monitoring and defibrillator equipment. The two aspects of the improved algorithm are compared with the single symbol time series analysis method. The experiment shows that the improved algorithm has better recognition performance, which is based on the sequence decomposition algorithm and the symbolic time series analysis, not only in the sample time or the symbolic level, but also in the separate use of symbolic time series analysis. The main innovation points in this paper are as follows: first, a new VT/VF detection algorithm based on the empirical mode decomposition sequence decomposition method and the symbolic time series analysis method is proposed. The research shows that the method has a high recognition degree to VT/VF. Second, the VT/VF based on the small wave analysis and the symbol time sequence analysis method is proposed. The new algorithm is detected. The research shows that the appropriate signal sampling rate is beneficial to the symbolic time series analysis method to excavate the essential characteristics of the sequence. The extraction of the symbol entropy by the decomposition sequence obtained by the wavelet decomposition has a higher recognition degree to the VT/VF compared with the EMD. Finally, the shortcomings of the symbolic time series analysis method are analyzed, and the problem of over coarse granulation is analyzed. The improvement is carried out and the improved algorithm is analyzed from many aspects. The experiment shows that the improved algorithm reduces the amount of calculation and has a higher recognition rate for the small sample in the case of keeping the VT/VF recognition rate basically unchanged.
【學(xué)位授予單位】：濟南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.7;TN911.6

【參考文獻】

相關(guān)期刊論文 前10條

1 李曉海;李東升;;環(huán)境因素影響下基于辛幾何譜分析法的結(jié)構(gòu)損傷識別[J];水利與建筑工程學(xué)報;2016年03期

2 劉長良;武英杰;甄成剛;;基于變分模態(tài)分解和模糊C均值聚類的滾動軸承故障診斷[J];中國電機工程學(xué)報;2015年13期

3 裘焱;吳亞鋒;李野;;應(yīng)用EMD分解下的Volterra模型提取機械故障特征[J];振動與沖擊;2010年06期

4 應(yīng)基光;王行愚;;基于EMD的左右手運動想象腦電信號分析研究[J];生物醫(yī)學(xué)工程學(xué)進展;2009年03期

5 程軍圣,于德介,楊宇;基于EMD的能量算子解調(diào)方法及其在機械故障診斷中的應(yīng)用[J];機械工程學(xué)報;2004年08期

6 楊世錫,胡勁松,吳昭同,嚴拱標;旋轉(zhuǎn)機械振動信號基于EMD的希爾伯特變換和小波變換時頻分析比較[J];中國電機工程學(xué)報;2003年06期

7 施壯華,沈模衛(wèi);心理學(xué)中腦電研究方法探討[J];心理科學(xué);2002年01期

8 孟欣,沈恩華,陳芳,顧凡及;腦電圖復(fù)雜度分析中的粗�；瘑栴} I.過分粗�；腿N復(fù)雜度的比較[J];生物物理學(xué)報;2000年04期

9 劉峰,鄭崇勛,伍曉宇;心率變異非線性動力學(xué)分析的狀態(tài)空間重構(gòu)方法[J];北京生物醫(yī)學(xué)工程;1999年03期

10 王揚,朱貽盛,古紅;利用雙譜分析法檢測心室纖顫信號[J];上海交通大學(xué)學(xué)報;1999年04期

相關(guān)碩士學(xué)位論文 前5條

1 訾彥勇;基于EMD的磁共振探測信號噪聲抑制方法研究[D];吉林大學(xué);2015年

2 周衛(wèi)佳;基于EMD和MP方法的醫(yī)學(xué)信號和圖像分析[D];北京郵電大學(xué);2010年

3 白家蓮;動態(tài)心電信號分析系統(tǒng)設(shè)計[D];中南大學(xué);2009年

4 梁崴巍;基于小波變換的心電信號預(yù)處理與特征識別算法[D];中國醫(yī)科大學(xué);2009年

5 張文s，

本文編號：2009352