本文選題：胎心宮縮圖　切入點：胎心率基線　出處：《暨南大學(xué)》2015年碩士論文

【摘要】：近年來,我國的出生缺陷率持續(xù)上升,計劃生育、優(yōu)生優(yōu)育國策面臨嚴(yán)峻挑戰(zhàn)。實施胎兒監(jiān)護(hù),有助于及時發(fā)現(xiàn)胎兒異常,從而降低出生缺陷率和嬰兒死亡率。胎心宮縮圖(CTG)是目前最常見的胎兒監(jiān)護(hù)方法。通過分析CTG信號,可以對胎兒的健康狀況進(jìn)行評估。然而,很多醫(yī)護(hù)工作者判讀CTG的水平不足,容易誤判而做出不當(dāng)?shù)呐R床決策。CTG計算機輔助分析的出現(xiàn),在一定程度上緩解了這個問題。但是,由于CTG的復(fù)雜性以及參數(shù)定義不夠嚴(yán)謹(jǐn),計算機分析仍存在很多問題,參數(shù)識別的準(zhǔn)確率有待提高。為此,本文研究CTG關(guān)鍵參數(shù)的提取方法,以提高參數(shù)識別的準(zhǔn)確率和效率;在此基礎(chǔ)上,本文進(jìn)一步研究胎兒狀態(tài)評估方法,提出一種更為有效的評估方法。本文的主要研究內(nèi)容如下:(1)胎心率基線估計算法的研究。針對現(xiàn)有算法準(zhǔn)確性或效率不高的問題,本文提出了一種結(jié)合胎動的胎心率基線估計算法。在該算法中,利用胎動確定胎心率曲線中加速段的位置,將其去除后進(jìn)行基線估計,再對基線進(jìn)行修正。將該算法與兩種現(xiàn)有算法對比,結(jié)果表明:在分析準(zhǔn)確性方面,本文算法明顯勝過參照算法一,稍微優(yōu)于參照算法二;而在計算效率方面,本文算法與參照算法一相差不大,但遠(yuǎn)高于參照算法二。(2)宮縮曲線特征提取算法的研究。本文研究了宮縮基線和宮縮波識別方法,并提出了相應(yīng)的新方法。與一種現(xiàn)有的宮縮基線估計算法相比,本文算法可得到更平滑的基線,并且能實現(xiàn)斷點檢測,從而使最終結(jié)果更合理。本文結(jié)合圖像膨脹原理和宮縮波形態(tài)學(xué)分析,提出了一種宮縮波識別算法。通過與一種現(xiàn)有算法作對比,結(jié)果顯示:本文算法的整體識別效果好于參照算法,且錯判率、漏判率均低于參照算法。同時,本文還設(shè)計了一種宮縮狀態(tài)實時識別方法,可實現(xiàn)四種狀態(tài)的實時判別。通過比較本算法的實時分析結(jié)果與醫(yī)生的事后分析結(jié)果,發(fā)現(xiàn)兩者吻合度很高。而且,該算法可滿足實時分析對計算效率的要求。(3)胎兒狀態(tài)評估方法的研究。針對目前計算機輔助分析系統(tǒng)直接套用CTG分類標(biāo)準(zhǔn)而導(dǎo)致胎兒狀態(tài)分析結(jié)果不準(zhǔn)的問題,本文根據(jù)文獻(xiàn)調(diào)研和實驗結(jié)果對CTG分類標(biāo)準(zhǔn)進(jìn)行修正,并結(jié)合模糊集合的思想,計算各個CTG參數(shù)對不同狀態(tài)的隸屬度,并使用歐幾里得距離衡量CTG信號與三種標(biāo)準(zhǔn)狀態(tài)間的差距,從而實現(xiàn)胎兒狀態(tài)的分析。實驗結(jié)果表明,與直接應(yīng)用分類標(biāo)準(zhǔn)的方法相比,本文方法可識別出更多的正常類信號,且該方法的特異度和陽性預(yù)測值遠(yuǎn)高于參照方法,總體準(zhǔn)確率也明顯高于參照方法。
[Abstract]:In recent years, the rate of birth defects in China has been rising, and the national policy of family planning and eugenics is facing severe challenges. The implementation of fetal monitoring is conducive to the timely detection of fetal abnormalities. Thus reducing the rate of birth defects and infant mortality. CTG is the most common method of fetal monitoring at present. By analyzing the CTG signal, the health of the fetus can be evaluated. However, Many health care workers interpret the level of CTG is insufficient, easy to misjudge and make improper clinical decision. CTG computer-aided analysis to some extent alleviates this problem. However, because of the complexity of CTG and parameter definition is not strict. There are still many problems in computer analysis, and the accuracy of parameter identification needs to be improved. In order to improve the accuracy and efficiency of parameter identification, this paper studies the extraction method of key parameters of CTG. In this paper, we further study the fetal state assessment method and propose a more effective evaluation method. The main contents of this paper are as follows: 1) the research on the baseline estimation algorithm of fetal heart rate. In this paper, an algorithm of fetal heart rate baseline estimation combined with fetal movement is proposed. In this algorithm, the position of accelerated segment in the fetal heart rate curve is determined by fetal movement, and the baseline estimation is carried out after removing it. The results show that the proposed algorithm is superior to the reference algorithm 1, slightly better than the reference algorithm 2 in terms of accuracy of analysis, and the efficiency of the algorithm is higher than that of the reference algorithm 2. The difference between this algorithm and reference algorithm one is not different, but much higher than that of reference algorithm 2. 2) the method of feature extraction of uterine contraction curve is studied in this paper. The method of identifying uterine contraction baseline and uterine contraction wave is studied in this paper. Compared with one of the existing base-line estimation algorithms, the proposed algorithm can obtain a smoother baseline and can detect breakpoints. Combining the principle of image expansion and morphological analysis of uterine constriction wave, this paper proposes an algorithm for recognition of uterine constriction wave, which is compared with one of the existing algorithms. The results show that the overall recognition effect of this algorithm is better than that of the reference algorithm, and the error rate and missing rate are lower than that of the reference algorithm. At the same time, a real-time recognition method of uterine contraction is designed. The real-time discriminant of four states can be realized. By comparing the real time analysis results of this algorithm with the results of doctors' post event analysis, it is found that the degree of agreement between the two methods is very high. The algorithm can meet the requirements of real-time analysis for computing efficiency. It can be used to study the method of fetal state evaluation. Aiming at the problem that the current computer-aided analysis system directly applies CTG classification standard, the result of fetal state analysis is inaccurate. In this paper, the classification standard of CTG is modified according to the literature investigation and experimental results, and the membership degree of each CTG parameter to different states is calculated by combining the idea of fuzzy set. The Euclidean distance is used to measure the difference between the CTG signal and the three standard states, so that the fetal state can be analyzed. The experimental results show that the proposed method can recognize more normal signals than the direct classification method. The specificity and positive predictive value of the method were much higher than that of the reference method, and the overall accuracy was significantly higher than that of the reference method.
【學(xué)位授予單位】：暨南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R714.5

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