本文關(guān)鍵詞： 手術(shù)監(jiān)測(cè) 生理信號(hào) 麻醉深度評(píng)估 腦電雙頻指數(shù) 排列組合熵　出處：《武漢理工大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：為了能夠更深入地研究臨床手術(shù),許多醫(yī)療科研機(jī)構(gòu)都需要收集大量的手術(shù)記錄數(shù)據(jù),建立手術(shù)醫(yī)療案例數(shù)據(jù)庫(kù)。包括心率、腦電雙頻指數(shù)、腦電圖、心電圖、呼吸頻率、血氧濃度、體溫、皮膚導(dǎo)電度等生理監(jiān)測(cè)信號(hào),它們反映了病人的主要生理活動(dòng)特征。尤其是手術(shù)期間病人處于麻醉狀態(tài),生理監(jiān)測(cè)信號(hào)能夠反映出病人對(duì)手術(shù)的反應(yīng),可以記錄和還原病人在手術(shù)期間的生理變化過(guò)程,是醫(yī)生手術(shù)實(shí)施過(guò)程中的重要參照信息。目前,手術(shù)生理監(jiān)測(cè)信號(hào)采集主要靠人工完成,這種方法浪費(fèi)大量的人力并且效率低下。同時(shí),市場(chǎng)上麻醉深度監(jiān)測(cè)儀價(jià)格昂貴,麻醉深度評(píng)估算法保密,提高了手術(shù)器械成本,加重了患者醫(yī)療負(fù)擔(dān)。針對(duì)目前存在的這些問(wèn)題,本文主要完成了以下研究工作:(1)分析了現(xiàn)有人工生理監(jiān)測(cè)信號(hào)采集方法的不足,提出了一種自動(dòng)、實(shí)時(shí)的采集方案。建立一個(gè)基于WLAN的無(wú)線網(wǎng)絡(luò),用于信號(hào)的無(wú)線傳輸;設(shè)計(jì)一個(gè)基于MySQL的數(shù)據(jù)庫(kù)系統(tǒng),用于存儲(chǔ)手術(shù)生理數(shù)據(jù),用戶可以通過(guò)WEB瀏覽器進(jìn)行數(shù)據(jù)訪問(wèn)。(2)在實(shí)際的醫(yī)院環(huán)境下,利用Wifi Analyzer軟件測(cè)試WLAN網(wǎng)絡(luò)的信號(hào)覆蓋范圍及強(qiáng)度,利用IPerf測(cè)試WLAN數(shù)據(jù)傳輸速率。通過(guò)對(duì)比醫(yī)院麻醉醫(yī)師記錄數(shù)據(jù),測(cè)試系統(tǒng)數(shù)據(jù)采集的準(zhǔn)確性。測(cè)試結(jié)果表明,WLAN信號(hào)覆蓋范圍較廣,信號(hào)強(qiáng)度較強(qiáng),數(shù)據(jù)傳輸速度較快,采集準(zhǔn)確率較高,各指標(biāo)都達(dá)到了應(yīng)用需求。(3)對(duì)生理監(jiān)測(cè)信號(hào)實(shí)時(shí)采集系統(tǒng)中獲取的數(shù)據(jù),進(jìn)行麻醉深度分析。提出一種基于排列組合熵的麻醉深度評(píng)估算法,該算法抗噪聲能力強(qiáng),時(shí)間復(fù)雜度低,運(yùn)算速度快。利用該算法對(duì)20例手術(shù)生理數(shù)據(jù)進(jìn)行麻醉評(píng)估,并將評(píng)估結(jié)果與BIS、專(zhuān)家評(píng)估清醒度分別進(jìn)行對(duì)比,證實(shí)了算法有效性和優(yōu)勢(shì)。本文設(shè)計(jì)的實(shí)時(shí)手術(shù)生理監(jiān)測(cè)信號(hào)采集系統(tǒng),可完成數(shù)據(jù)的記錄、傳輸、存儲(chǔ)、管理等功能。同時(shí),提出的麻醉深度評(píng)估算法,對(duì)采集到的手術(shù)生理監(jiān)測(cè)數(shù)據(jù)進(jìn)行了麻醉深度評(píng)估,結(jié)果有效地反映病人的麻醉狀態(tài)。
[Abstract]:In order to study clinical surgery more deeply, many medical research institutions need to collect a large amount of data of operation records and establish a database of surgical medical cases, including heart rate, bispectral index of EEG, electroencephalogram, electrocardiogram. Respiratory frequency, blood oxygen concentration, body temperature, skin conductivity and other physiological monitoring signals, which reflect the main physiological characteristics of the patient, especially during the operation in a state of anesthesia. Physiological monitoring signals can reflect the response of patients to surgery and can record and reduce the physiological changes of patients during operation. It is an important reference information in the process of doctors' operation. The acquisition of physiological monitoring signals mainly depends on manual, this method waste a lot of manpower and low efficiency. At the same time, the depth of anesthesia monitor is expensive in the market, and the anesthetic depth evaluation algorithm is confidential. In view of these problems, this paper mainly completed the following research work: 1) analyzed the shortcomings of the existing artificial physiological monitoring signal collection methods. In this paper, an automatic and real-time acquisition scheme is proposed, and a wireless network based on WLAN is established for wireless signal transmission. A database system based on MySQL is designed to store surgical physiological data. Users can access the data through WEB browser in the actual hospital environment. The signal coverage and intensity of WLAN network are tested by Wifi Analyzer software. The data transmission rate of WLAN was measured by IPerf. By comparing the data recorded by the anesthesiologist in hospital, the accuracy of data acquisition in the system was tested. The test results showed that the coverage of WLAN signal was wide. The signal intensity is stronger, the data transmission speed is faster, the collection accuracy is higher, each index has reached the application demand. 3) to the physiological monitoring signal real-time acquisition system to obtain the data. An algorithm based on permutation and combination entropy is proposed to evaluate the depth of anesthesia. The algorithm has strong anti-noise ability and low time complexity. The algorithm was used to evaluate the physiological data of 20 cases of surgery, and the results were compared with the BIS and the expert evaluation of sobriety. The effectiveness and advantages of the algorithm are confirmed. The real-time surgical physiological monitoring signal acquisition system designed in this paper can complete data recording, transmission, storage, management and other functions. At the same time, the proposed anesthetic depth evaluation algorithm. The anaesthesia depth of the collected monitoring data of surgery physiology was evaluated, and the results reflected the anaesthesia state of the patients effectively.
【學(xué)位授予單位】：武漢理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：R61;TP274.2

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