手术生理监测信号实时采集系统设计及麻醉深度分析

发布时间：2018-01-20 16:55

本文关键词： 手术监测生理信号麻醉深度评估脑电双频指数排列组合熵　出处：《武汉理工大学》2015年硕士论文　论文类型：学位论文

【摘要】：为了能够更深入地研究临床手术,许多医疗科研机构都需要收集大量的手术记录数据,建立手术医疗案例数据库。包括心率、脑电双频指数、脑电图、心电图、呼吸频率、血氧浓度、体温、皮肤导电度等生理监测信号,它们反映了病人的主要生理活动特征。尤其是手术期间病人处于麻醉状态,生理监测信号能够反映出病人对手术的反应,可以记录和还原病人在手术期间的生理变化过程,是医生手术实施过程中的重要参照信息。目前,手术生理监测信号采集主要靠人工完成,这种方法浪费大量的人力并且效率低下。同时,市场上麻醉深度监测仪价格昂贵,麻醉深度评估算法保密,提高了手术器械成本,加重了患者医疗负担。针对目前存在的这些问题,本文主要完成了以下研究工作:(1)分析了现有人工生理监测信号采集方法的不足,提出了一种自动、实时的采集方案。建立一个基于WLAN的无线网络,用于信号的无线传输;设计一个基于MySQL的数据库系统,用于存储手术生理数据,用户可以通过WEB浏览器进行数据访问。(2)在实际的医院环境下,利用Wifi Analyzer软件测试WLAN网络的信号覆盖范围及强度,利用IPerf测试WLAN数据传输速率。通过对比医院麻醉医师记录数据,测试系统数据采集的准确性。测试结果表明,WLAN信号覆盖范围较广,信号强度较强,数据传输速度较快,采集准确率较高,各指标都达到了应用需求。(3)对生理监测信号实时采集系统中获取的数据,进行麻醉深度分析。提出一种基于排列组合熵的麻醉深度评估算法,该算法抗噪声能力强,时间复杂度低,运算速度快。利用该算法对20例手术生理数据进行麻醉评估,并将评估结果与BIS、专家评估清醒度分别进行对比,证实了算法有效性和优势。本文设计的实时手术生理监测信号采集系统,可完成数据的记录、传输、存储、管理等功能。同时,提出的麻醉深度评估算法,对采集到的手术生理监测数据进行了麻醉深度评估,结果有效地反映病人的麻醉状态。
[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.
【学位授予单位】：武汉理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：R61;TP274.2

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