本文關鍵詞： 手術監(jiān)測 生理信號 麻醉深度評估 腦電雙頻指數(shù) 排列組合熵　出處：《武漢理工大學》2015年碩士論文　論文類型：學位論文

【摘要】：為了能夠更深入地研究臨床手術,許多醫(yī)療科研機構都需要收集大量的手術記錄數(shù)據(jù),建立手術醫(yī)療案例數(shù)據(jù)庫。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、皮膚導電度等生理監(jiān)測信號,它們反映了病人的主要生理活動特征。尤其是手術期間病人處于麻醉狀態(tài),生理監(jiān)測信號能夠反映出病人對手術的反應,可以記錄和還原病人在手術期間的生理變化過程,是醫(yī)生手術實施過程中的重要參照信息。目前,手術生理監(jiān)測信號采集主要靠人工完成,這種方法浪費大量的人力并且效率低下。同時,市場上麻醉深度監(jiān)測儀價格昂貴,麻醉深度評估算法保密,提高了手術器械成本,加重了患者醫(yī)療負擔。針對目前存在的這些問題,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)測信號采集方法的不足,提出了一種自動、實時的采集方案。建立一個基于WLAN的無線網(wǎng)絡,用于信號的無線傳輸;設計一個基于MySQL的數(shù)據(jù)庫系統(tǒng),用于存儲手術生理數(shù)據(jù),用戶可以通過WEB瀏覽器進行數(shù)據(jù)訪問。(2)在實際的醫(yī)院環(huán)境下,利用Wifi Analyzer軟件測試WLAN網(wǎng)絡的信號覆蓋范圍及強度,利用IPerf測試WLAN數(shù)據(jù)傳輸速率。通過對比醫(yī)院麻醉醫(yī)師記錄數(shù)據(jù),測試系統(tǒng)數(shù)據(jù)采集的準確性。測試結果表明,WLAN信號覆蓋范圍較廣,信號強度較強,數(shù)據(jù)傳輸速度較快,采集準確率較高,各指標都達到了應用需求。(3)對生理監(jiān)測信號實時采集系統(tǒng)中獲取的數(shù)據(jù),進行麻醉深度分析。提出一種基于排列組合熵的麻醉深度評估算法,該算法抗噪聲能力強,時間復雜度低,運算速度快。利用該算法對20例手術生理數(shù)據(jù)進行麻醉評估,并將評估結果與BIS、專家評估清醒度分別進行對比,證實了算法有效性和優(yōu)勢。本文設計的實時手術生理監(jiān)測信號采集系統(tǒng),可完成數(shù)據(jù)的記錄、傳輸、存儲、管理等功能。同時,提出的麻醉深度評估算法,對采集到的手術生理監(jiān)測數(shù)據(jù)進行了麻醉深度評估,結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.
【學位授予單位】：武漢理工大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R61;TP274.2

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