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Power index of the inspiratory flow signal as a predictor of weaning in intensive care units

机译:吸气流量信号的功率指数可作为重症监护病房断奶的预测指标

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Disconnection from mechanical ventilation, called the weaning process, is an additional difficulty in the management of patients in intensive care units (ICU). Unnecessary delays in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we propose an extubation index based on the power of the respiratory flow signal (Pi). A total of 132 patients on weaning trials were studied: 94 patients with successful trials (group S) and 38 patients who failed to maintain spontaneous breathing and were reconnected (group F). The respiratory flow signals were processed considering the following three stages: a) zero crossing detection of the inspiratory phase, b) inflection point detection of the flow curve during the inspiratory phase, and c) calculation of the signal power on the time instant indicated by the inflection point. The zero crossing detection was performed using an algorithm based on thresholds. The inflection points were marked considering the zero crossing of the second derivative. Finally, the inspiratory power was calculated from the energy contained over the finite time interval (between the instant of zero crossing and the inflection point). The performance of this parameter was evaluated using the following classifiers: logistic regression, linear discriminant analysis, the classification and regression tree, Naive Bayes, and the support vector machine. The best results were obtained using the Bayesian classifier, which had an accuracy, sensitivity and specificity of 87%, 90% and 81% respectively.
机译:与机械通气的断开(称为断奶过程)是重症监护病房(ICU)患者管理中的另一个困难。停药过程中不必要的延误以及过早进行的断奶试验是不希望的。在这项研究中,我们提出了基于呼吸流量信号(Pi)的力量的拔管指数。总共对132例断奶试验患者进行了研究:94例试验成功的患者(S组)和38例未能保持自主呼吸并重新连接的患者(F组)。考虑以下三个阶段处理呼吸流量信号:a)吸气阶段的过零检测,b)吸气阶段期间流量曲线的拐点检测,以及c)在由指示的瞬时计算信号功率拐点。使用基于阈值的算法执行过零检测。考虑到二阶导数的零交叉,标记了拐点。最后,根据在有限时间间隔(过零时刻和拐点之间)中包含的能量来计算吸气功率。使用以下分类器评估此参数的性能:逻辑回归,线性判别分析,分类和回归树,朴素贝叶斯和支持向量机。使用贝叶斯分类器可获得最佳结果,其准确性,敏感性和特异性分别为87%,90%和81%。

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