本文選題：BCI技術(shù) + 面孔識別�。� 參考：《廣州中醫(yī)藥大學(xué)》2017年碩士論文

【摘要】：目的:1.通過運(yùn)用BCI檢測意識障礙(Disorders of consciousness,DOC)患者意識水平,將所得在線準(zhǔn)確率與隨訪獲得的格拉斯哥結(jié)局量表(Glasgow outcome scale,GOS)評分進(jìn)行相關(guān)性分析,判斷本研究所運(yùn)用的基于面孔識別的腦機(jī)接口(Brain-computer interface,BCI)技術(shù)對DOC患者預(yù)后評估的價值;2.通過分別做基于面孔識別的BCI在線準(zhǔn)確率結(jié)果及CRS-R評分結(jié)果與GOS評分等級的相關(guān)性分析,探討B(tài)CI在DOC患者預(yù)后評估中是否較CRS-R評分更為準(zhǔn)確、客觀,能否作為行為學(xué)量表補(bǔ)充,提高診斷的準(zhǔn)確性。方法:對23名DOC患者進(jìn)行基于面孔識別的BCI檢測,每個患者每周檢測兩次,共進(jìn)行5次檢測;每次檢測包括兩個部分,即訓(xùn)練部分和測試部分,每部分包含10次小測試,每一次的小測試中相片以隨機(jī)的方式進(jìn)行閃爍,將5次測試所得在線準(zhǔn)確率進(jìn)行平均得出最終在線準(zhǔn)確率,檢測結(jié)束3個月后或是發(fā)病6個月后對患者進(jìn)行電話隨訪,行GOS評分。第一部分以GOS評分等級為結(jié)局指標(biāo),做二分類的Logistic回歸分析。第二部分在測試前或測試后24h內(nèi)選取患者狀態(tài)最佳的時刻予以行CRS-R評分。將BCI在線準(zhǔn)確率結(jié)果及檢測前后的CRS-R評分與隨訪獲得的GOS評分分別進(jìn)行spearman相關(guān)性分析,P0.05時認(rèn)為與GOS評分顯著相關(guān)。再將BCI及CRS-R評分共同納入建立Logistic回歸模型,判斷二者聯(lián)合是否能更準(zhǔn)確的評估預(yù)后。結(jié)果:第一部分做BCI在線準(zhǔn)確率與GOS評分等級的Logistic回歸分析,得出相關(guān)系數(shù)為 8.97,對應(yīng)的P=0.0130.05,Hosmer-Lemeshow 檢驗結(jié)果 χ2為 0.591,對應(yīng)的P=0.988。BCI對良好結(jié)局的預(yù)測準(zhǔn)確率為75%,對較差結(jié)局的預(yù)測準(zhǔn)確率為93.3%,對DOC患者總預(yù)后的預(yù)測準(zhǔn)確率為84%。做出BCI在線準(zhǔn)確率對預(yù)后結(jié)局預(yù)測概率的ROC曲線,曲線下面積為0.925,95%置信區(qū)間為[0.806,0.966],提示BCI在線準(zhǔn)確率可以較好地評估DOC患者的預(yù)后。第二部分將入院時及BCI檢測前后24h內(nèi)的CRS-R評分分別與GOS評分做Spearman相關(guān)性分析,入院時的CRS-R與GOS相關(guān)系數(shù)r值為0.354,對應(yīng)的P=0.030.05;檢測時的CRS-R評分與GOS評分相關(guān)系數(shù)r為0.505,對應(yīng)的P=0.0140.05。CRS-R評分與GOS評分顯著相關(guān)。BCI在線準(zhǔn)確率與GOS評分做Spearman相關(guān)性分析,相關(guān)系數(shù)r為0.638,P=0.0010.05,二者顯著相關(guān)。通過比較相關(guān)系數(shù)的大小,可得出BCI在線準(zhǔn)確率比CRS-R評分與GOS評分相關(guān)性更強(qiáng)。再做BCI在線準(zhǔn)確率及CRS-R與GOS評分等級的Logistic回歸分析,BCI在線準(zhǔn)確率對應(yīng)的回歸系數(shù)為6.301,P= 0.030.05,CRS-R評分的回歸系數(shù)為 1.788,P= 0.0410.05。Hosmer-Lemeshow 檢驗結(jié)果 χ2 為 3.146,P= 0.925。二者結(jié)合的模型對良好預(yù)后的預(yù)測準(zhǔn)確率為93%,對較差預(yù)后的預(yù)測準(zhǔn)確率為80.7%,對DOC患者預(yù)后結(jié)局總預(yù)測準(zhǔn)確率為87%。ROC曲線下的面積為0.943。與第一部分得出BCI單獨(dú)預(yù)測DOC患者預(yù)后結(jié)局的準(zhǔn)確率相比較,二者聯(lián)合準(zhǔn)確率更高。結(jié)論:1.基于面孔識別的BCI能夠從腦反應(yīng)的角度更為客觀地檢測DOC患者意識水平,并且能夠很好地評估患者預(yù)后,在線準(zhǔn)確率能夠作為反映DOC患者腦功能及預(yù)后的一項客觀指標(biāo),準(zhǔn)確率達(dá)0.64以上的患者意識恢復(fù)的可能性較大;2.在DOC患者意識水平診斷及預(yù)后評估方面基于面孔識別的多模態(tài)腦機(jī)接口技術(shù)比目前臨床常用的CRS-R量表準(zhǔn)確性更高,二者結(jié)合能夠更好地評估DOC患者的預(yù)后,BCI技術(shù)能夠作為臨床行為學(xué)量表的補(bǔ)充。
[Abstract]:Objective: 1. through the use of BCI to detect the consciousness level of Disorders of consciousness (DOC), the correlation analysis between the online accuracy rate and the Glasgow Outcome Scale (Glasgow outcome scale, GOS) obtained from the follow-up was carried out to determine the face recognition based brain machine interface (Brain-computer interface) used in this study (Brain-computer interface). The value of BCI) technology for evaluating the prognosis of DOC patients; 2. through the correlation analysis of the BCI online accuracy results based on face recognition and the correlation between the CRS-R score and the GOS grade, it is discussed whether BCI is more accurate than the CRS-R score in the prognosis assessment of DOC patients. It is objective, whether it can be supplemented by the behavioral scale, and improve the accuracy of the diagnosis. Methods: 23 DOC patients were detected by face recognition based BCI test. Each patient was detected two times a week, with a total of 5 tests. Each test included two parts, the training part and the test section, each part contained 10 small tests. Each of the small tests was flickered with the machine. The online accuracy rate of the 5 tests was obtained. On average, the final online accuracy was obtained. 3 months after the end of the test or 6 months after the onset of the disease, the patients were followed up by telephone, and the GOS score was performed. The first part took the GOS score grade as the outcome index, and did the two classification of Logistic regression analysis. The second part took the CRS-R evaluation before or after the test of the best state of the patient. The results of BCI online accuracy, the CRS-R score before and after detection and the GOS score of the follow-up were analyzed with Spearman correlation respectively. P0.05 was considered to be significantly related to the GOS score. Then BCI and CRS-R scores were incorporated into the Logistic regression model to determine whether the combination of the two would be more accurate to evaluate the prognosis. The first part was B. The Logistic regression analysis of CI online accuracy and GOS grade showed that the correlation coefficient was 8.97, the corresponding P=0.0130.05, the Hosmer-Lemeshow test result chi 2 was 0.591, the corresponding P=0.988.BCI for the good outcome was 75%, the prediction accuracy of the poor outcome was 93.3%, and the prediction accuracy for the total prognosis of DOC patients was 84%.. To make the ROC curve of the prediction probability of BCI online accuracy on prognosis, the area under the curve is 0.925,95% confidence interval [0.806,0.966], suggesting that the BCI online accuracy can better evaluate the prognosis of DOC patients. The second part will be admitted to the hospital and the CRS-R score in 24h before and after BCI detection and GOS score for Spearman correlation analysis, admission to hospital. The correlation coefficient r value of CRS-R and GOS was 0.354, corresponding P=0.030.05, and the correlation coefficient r of CRS-R score and GOS score was 0.505, and the corresponding P=0.0140.05.CRS-R score and GOS score were significantly related to Spearman correlation analysis of.BCI online accuracy and GOS score, and the correlation coefficient was 0.638, and the comparison was significant. Through comparison, the correlation coefficient was significantly correlated. The correlation coefficient can be found that the BCI online accuracy rate is more correlated with the GOS score than the CRS-R score. In the Logistic regression analysis of BCI online accuracy and CRS-R and GOS grade, the regression coefficient corresponding to the BCI online accuracy is 6.301, P= 0.030.05, and CRS-R score is 1.788. The predictive accuracy of the combined model of P= 0.925. two for good prognosis was 93%, the prediction accuracy for poor prognosis was 80.7%. The total prediction accuracy for the prognosis of DOC patients was 87%.ROC curve, and the area of the 87%.ROC curve was 0.943. compared with the accuracy rate of the first part of BCI to predict the prognosis of DOC patients by BCI alone. Conclusion: 1. BCI based on face recognition can detect the consciousness level of DOC patients more objectively from the angle of brain response, and can evaluate the prognosis of patients well. The online accuracy can be used as an objective indicator to reflect the brain function and prognosis of DOC patients, and the possibility of consciousness recovery is more than 0.64. 2. the multimodal brain machine interface technology based on face recognition is more accurate in DOC patients' consciousness level diagnosis and prognosis assessment than the current clinical CRS-R scale. The combination of the two can better evaluate the prognosis of DOC patients, and the BCI technique can be used as a supplement to the clinical behavioral scale.

【學(xué)位授予單位】：廣州中醫(yī)藥大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R741.044

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