INTELLIGENT FUZZY ADAPTIVE CONTROL STRATEGIES FOR ANESTHESIA

麻醉智能模糊自适应控制策略

• 批准号: 6191973
• 负责人: phil d rahbar maghsoundi
• 金额: $ 4.76万
• 依托单位: ST. MARY'S UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-06-01 至 2000-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6191973
• 关键词: artificial intelligence; auditory stimulus; automated medical record system; clinical research; computer assisted medical decision making; computer assisted patient care; computer simulation; data collection methodology /evaluation; dosage; drug adverse effect; drug delivery systems; drug screening /evaluation; electroencephalography; general anesthesia; health economics; hemodynamics; human subject; mathematical model; method development; model design /development; neurosurgery; patient care management; stimulus /response

This study will collect hemodynamic and physiological data in conjunction with raw EEG and Auditory Evoked Responses from patients undergoing general anesthesia for surgical procedures. This data will be analyzed off-line for correlation of EEG and hemodynamic changes that would be interpreted clinically as changes in anesthesia depth. Utilizing this information, digital signal processing techniques, adaptive control theory and fuzzy logic concepts will be applied to develop intelligent fuzzy adaptive control strategies for anesthesia. These control strategies will be evaluated by their ability to achieve the desired depth of anesthesia while maintaining patient safety. Quadratic cost functions related to the deviation of the hemodynamic and physiological/neurological indices from their safe and desired clinical values will be developed and used to determine anesthetic induced insult/trauma to the patient. Fuzzy cost functions will determine deviation of the estimated depth of anesthesia from the desired depth. These cost functions will form a basis for comparing the performance of the implemented control strategy to that of the attending anesthesiologist. The difference between the estimated and desired depth of anesthesia and the patients weighted Physiological/Neurological trends will be used to calculate the recommended anesthesia. The difference between the estimated and desired depth of anesthesia and the patients weighted Physiological/Neurological trends will be used to calculate the recommended anesthesia management scheme. Anesthetic doses will be monitored for safety and then implemented by the attending anesthesiologist. This study will enroll thirty adult patients of both sexes who are ASA physical status II or I and are scheduled for lumbar discectomy and/or laminectomy under general anesthesia. Surface EEC electrodes and ear phones for auditory stimulus will be placed and standard monitors of ECG, blood pressure, pulse oximetry and respiratory gas analysis will be connected. Baseline awake values will be obtained. Each patient's anesthetic induction will be conducted as determined by the staff anesthesiologist. The EEG, Evoked potentials, ECG, Pulse Rate, Blood Pressure, and the respiratory information will be collected from the OR instrumentation using a high-speed real-time computer. The specific aims and benefits will be reduced workload instrumentation using a high-speed real-time computer. The specific aims and benefits will be reduced workload through: 1) Intelligent and clinically relevant presentation of patient status information; 2) Real-time presentation/recording of succinct clinically relevant presentation of patient status information; 2) Real-time presentation/recording of succinct context sensitive patient/anesthetic status alarms and trend analysis: 3) interpretation of patient status into high-level recommendations on patient well-being and anesthetic management and 4) integrated control of anesthetic gents and a patient gases by the IAM/MS. Also, recorded surgical time histories and embedded reasoning could form the foundation of a computerized anesthesiologist patient simulator.

这项研究将收集血液动力学和生理数据，并结合原始脑电和听觉诱发反应，来自手术过程中接受全身麻醉的患者。这些数据将被离线分析，以确定脑电和血流动力学变化的相关性，这些变化在临床上将被解释为麻醉深度的变化。利用这些信息，将应用数字信号处理技术、自适应控制理论和模糊逻辑概念来开发智能模糊自适应麻醉控制策略。这些控制策略将根据它们在维护患者安全的同时达到所需麻醉深度的能力进行评估。将建立与血流动力学和生理/神经指标偏离其安全和期望的临床值有关的二次成本函数，并用于确定麻醉对患者的伤害/伤害。模糊成本函数将确定估计的麻醉深度与期望深度的偏差。这些成本函数将构成比较所实施的控制策略与主治麻醉师的绩效的基础。估计和期望的麻醉深度与患者加权的生理/神经趋势之间的差值将被用于计算推荐的麻醉。估计和期望的麻醉深度与患者加权的生理/神经趋势之间的差值将被用于计算推荐的麻醉管理方案。麻醉剂量将进行安全监测，然后由主治麻醉师实施。这项研究将纳入30名成年患者，他们的身体状况为II或I级，并计划在全身麻醉下接受腰椎间盘摘除术和/或椎板切除术。将放置用于听觉刺激的表面EEC电极和耳机，并连接标准的心电、血压、脉搏血氧仪和呼吸气体分析监测器。将获得基线唤醒值。每位患者的麻醉诱导将由工作人员麻醉师决定。脑电、诱发电位、心电、脉率、血压和呼吸信息将使用高速实时计算机从手术室仪器中收集。具体目标和好处将是使用高速实时计算机减少工作量的仪器。具体目标和好处将通过以下方式减少工作量：1)患者状态信息的智能和临床相关的呈现；2)患者状态信息的简明临床相关呈现的实时呈现/记录；2)简明的上下文敏感的患者/麻醉状态警报和趋势分析的实时呈现/记录：3)将患者状态解释为关于患者健康和麻醉管理的高级建议，以及4)由IAM/MS对麻醉剂和患者气体的集成控制。此外，记录的手术时间历史和嵌入式推理可以形成计算机化的麻醉师患者模拟器的基础。