本文選題：語音信號(hào)分析 + 啼哭　； 參考：《上海交通大學(xué)》2014年博士論文

【摘要】：研究背景和目的尋找疼痛評(píng)估的客觀指標(biāo)一直是學(xué)術(shù)界難以解決的關(guān)鍵問題。尤其對(duì)于嬰幼兒,因其發(fā)育尚不完善,無法進(jìn)行疼痛的自身描述,導(dǎo)致在臨床上缺乏簡單、有效的嬰幼兒疼痛評(píng)估標(biāo)準(zhǔn)。啼哭是多種嬰幼兒疼痛行為學(xué)評(píng)估量表中均包含的特征之一,且有研究表明,嬰幼兒疼痛與非疼痛的啼哭聲存在不同。但是疼痛所導(dǎo)致的啼哭信號(hào)具體包含怎樣的聲學(xué)特征,且這種聲學(xué)特征是否能作為評(píng)估嬰幼兒術(shù)后疼痛的標(biāo)準(zhǔn)尚未見報(bào)道。我們擬通過提取6個(gè)月以內(nèi)嬰兒術(shù)后疼痛哭聲信號(hào)特征,構(gòu)建疼痛哭聲識(shí)別模型應(yīng)用于嬰兒術(shù)后疼痛評(píng)估,以期為臨床探索更客觀、便捷的嬰幼兒疼痛評(píng)估工具提供研究基礎(chǔ)。研究內(nèi)容和方法以6個(gè)月以內(nèi)擇期手術(shù)的嬰兒為研究對(duì)象。記錄嬰兒手術(shù)前啼哭數(shù)據(jù),以及從嬰兒手術(shù)后進(jìn)入麻醉蘇醒室至嬰兒蘇醒后離開蘇醒室這一時(shí)間段的啼哭信號(hào)。同時(shí)采用FLACC(Face,Legs,Activity,Cry,Consolability)評(píng)分量表對(duì)嬰兒蘇醒后的疼痛程度進(jìn)行評(píng)估。以Adobe audition3.0對(duì)啼哭聲音文件進(jìn)行初步處理,通過Praat語音分析軟件進(jìn)行特征性參數(shù)分析;以MFCC(Mel Frequency Cepstrum Coefficient)參數(shù)作為特征性參數(shù),以HMM(Hidden Markorv Model)模型作為訓(xùn)練模型,構(gòu)建疼痛識(shí)別模型,同時(shí)以FLACC為標(biāo)準(zhǔn),使用ROC(Receiver operating characteristic curve)曲線比較不同組合的HMM模型對(duì)嬰兒術(shù)后疼痛啼哭的識(shí)別效能,尋找最優(yōu)組合的疼痛識(shí)別模型,并檢驗(yàn)該模型對(duì)術(shù)后疼痛以及重度疼痛的識(shí)別效能。使用SPSS 19.0進(jìn)行數(shù)據(jù)處理和統(tǒng)計(jì)分析,計(jì)量資料以均數(shù)±標(biāo)準(zhǔn)差表示,數(shù)據(jù)均進(jìn)行正態(tài)性檢驗(yàn)和方差齊性檢驗(yàn),方差齊性的數(shù)據(jù)兩組間比較使用t檢驗(yàn),方差非齊性兩組間比較使用wilcoxon檢驗(yàn),多組間比較使用Kruskal-Wallis檢驗(yàn),p0.05差異具有統(tǒng)計(jì)學(xué)意義。使用R v 2.15.1統(tǒng)計(jì)作圖。研究結(jié)果本研究為前瞻性研究(臨床注冊(cè)碼:Chi CTR-OCH-14004648),共納入155名擇期手術(shù)嬰兒。1.嬰兒術(shù)后疼痛啼哭基頻為654.7±195.8Hz,顯著高于手術(shù)前啼哭基頻464.6±146.1Hz,p0.01。2.術(shù)后疼痛組啼哭的基頻(F0)、第一共振峰(F1),均方根(Root mean square,RMS)明顯高于無痛/輕度疼痛組(p0.01),而音節(jié)間歇時(shí)間(interval between Syllables,IS)顯著下降(p0.01)。3.組合為18state+12mixture數(shù)目的HMM模型對(duì)術(shù)后疼痛識(shí)別效能最大,其ROC曲線中的AUC(area under curve)為0.81±0.049,(95%CI:0.713-0.906),最佳截?cái)帱c(diǎn)為0.558,敏感度為80.0%,特異度為77.1%。該模型診斷重度疼痛的AUC為0.764±0.059,(95%CI:0.648-0.880),特異度僅50.9%,但其敏感度非常高,為91.3%。結(jié)論嬰兒術(shù)后的疼痛啼哭較非疼痛啼哭信號(hào)具有特征性的改變,這種改變可以被語音識(shí)別模型所探知,利用疼痛啼哭識(shí)別模型對(duì)嬰兒術(shù)后疼痛的鑒別具有一定的診斷價(jià)值,本研究為進(jìn)一步開發(fā)無創(chuàng)傷性、自動(dòng)化的、有效評(píng)估嬰幼兒術(shù)后疼痛的工具奠定了前期基礎(chǔ)。
[Abstract]:Research background and purpose of finding the objective index of pain assessment is a key problem that is difficult to solve in the academic community. Especially for infants, the lack of self description of pain because of its imperfect development leads to the lack of simple and effective evaluation of infant pain in clinical. Crying is the assessment of many kinds of infant pain behavior. One of the features contained in the table shows that infant pain is different from that of non painful crying. But what is the acoustic characteristic of the crying signal caused by pain, and whether this acoustic feature can be used as a standard for assessing postoperative pain in infants and young children is not yet reported. We are going to extract babies within 6 months. In order to provide the basis for the clinical exploration of more objective and convenient infant pain assessment tools, a new model of pain crying recognition is used to provide research basis for the clinical exploration of a more objective and convenient tool for evaluation of infant pain. The crying signals of the waking room of the baby after the baby's operation were entered into the awakening room, and the FLACC (Face, Legs, Activity, Cry, Consolability) rating scale was used to evaluate the degree of pain after the awakening of the baby. The initial treatment of the crying sound file of Adobe audition3.0 was carried out by the Praat speech analysis. The software carries out the characteristic parameter analysis; taking the parameters of MFCC (Mel Frequency Cepstrum Coefficient) as the characteristic parameter, and using the HMM (Hidden Markorv Model) model as the training model, the pain recognition model is built, and the ROC (Receiver) Cepstrum curve is used to compare the infant model to the baby. The ability to identify the pain and cry after the operation, find the best combination of the pain recognition model, and test the recognition effectiveness of the model for postoperative pain and severe pain. Data processing and statistical analysis are performed using SPSS 19. The measurement data are expressed with mean standard deviation, and the data are tested in normality, variance homogeneity test and homogeneity of variance. The data between the two groups were compared with the use of t test, the two groups of variance inhomogeneous using the Wilcoxon test, the multiple groups using Kruskal-Wallis test, the P0.05 difference was statistically significant. The R V 2.15.1 statistics were used. The results of this study were prospective study (clinical registration code: Chi CTR-OCH-14004648), including 155 selected operation infants. The basic frequency of pain and crying after operation for infant.1. was 654.7 + 195.8Hz, which was significantly higher than the basic frequency (F0) and the first resonance peak (F1) in the pain group after p0.01.2., and the mean square root (Root mean square, RMS) was significantly higher than that of the painless / mild pain group (P0.01). The HMM model with a P0.01.3. combination of 18state+12mixture was the most effective for postoperative pain recognition, and the AUC (area under curve) in the ROC curve was 0.81 + 0.049, (95%CI:0.713-0.906), the best truncation point was 0.558, the sensitivity was 80%, the specificity was 0.764 + 0.059 for the 77.1%. mode diagnosis of severe pain. The degree of sensitivity is only 50.9%, but its sensitivity is very high. It is a characteristic change in the pain crying of 91.3%. conclusion after operation. This change can be detected by the speech recognition model. The use of pain crying recognition model has a certain diagnostic value for the identification of postoperative pain in infants. This study is a further development of this study. Traumatic, automated, effective tools for evaluating postoperative pain in infants have laid a foundation for early surgery.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：R726.1

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本文編號(hào)：1935543