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首页> 外文期刊>The Canadian Journal of Neurological Sciences: le Journal Canadien des Sciences Neurologiques >Algorithm for Quantifying Frontal EMG Responsiveness for Sedation Monitoring
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Algorithm for Quantifying Frontal EMG Responsiveness for Sedation Monitoring

机译：镇静监测中量化额叶肌电反应性的算法

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Introduction: To study stimulation-related facial electromyographic (FEMG) activity in intensive care unit (ICU) patients, develop an algorithm for quantifying the FEMG activity, and to optimize the algorithm for monitoring the sedation state of ICU patients. Methods: First, the characteristics of FEMG response patterns related to vocal stimulation of 17 ICU patients were studied. Second, we collected continuous FEMG data from 30 ICU patients. Based on these data, we developed the Responsiveness Index (RI) algorithm that quantifies FEMG responses. Third, we compared the RI values with clinical sedation level assessments and adjusted algorithm parameters for best performance. Results: In patients who produced a clinically observed response to the vocal stimulus, the poststimulus FEMG power was 0.33 mu V higher than the prestimulus power. In nonresponding patients, there was no difference. The sensitivity and specificity of the developed RI for detecting deep sedation in the subgroup with low probability of encephalopathy were 0.90 and 0.79, respectively. Conclusion: Consistent FEMG patterns were found related to standard stimulation of ICU patients. A simple and robust algorithm was developed and good correlation with clinical sedation scores achieved in the development data.
机译：简介：为了研究重症监护病房（ICU）患者与刺激有关的面部肌电图（FEMG）活动，开发了量化FEMG活动的算法，并优化了用于监视ICU患者镇静状态的算法。方法：首先，研究了17例ICU患者与声音刺激相关的FEMG反应模式的特征。其次，我们收集了30位ICU患者的连续FEMG数据。基于这些数据，我们开发了量化FEMG响应的响应指数（RI）算法。第三，我们将RI值与临床镇静水平评估和调整后的算法参数进行比较，以获得最佳性能。结果：在临床上观察到的对声刺激产生反应的患者中，刺激后的FEMG功率比刺激前的功率高0.33μV。在无反应的患者中，没有差异。发达的RI在低脑病可能性亚组中检测深度镇静的敏感性和特异性分别为0.90和0.79。结论：发现一致的FEMG模式与ICU患者的标准刺激有关。开发了一种简单而强大的算法，并且与开发数据中获得的临床镇静分数具有良好的相关性。

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