Giraldo B, Arizmendi C, Romero E, Alquezar R, Caminal P, Benito S, Ballesteros D
Dept. of ESAII, Tech. Univ. of Catalonia, Barcelona, Spain.
Conf Proc IEEE Eng Med Biol Soc. 2006;2006:2195-8. doi: 10.1109/IEMBS.2006.259607.
One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation are studied: Group S with 32 patients with Successful trials and Group F with 32 patients that Failed to maintain spontaneous breathing and were reconnected. A performance of 64.56% of well classified patients was obtained using a neural network trained with the whole set of 35 features. After the application of a feature selection procedure (backward selection) 84.56% was obtained using only 8 of the 35 features.
重症监护中的挑战之一是机械通气的撤机过程。我们研究了在撤机试验期间能够维持自主呼吸的患者与未能维持自主呼吸的患者之间呼吸模式变异性的差异。在这项工作中，应用神经网络来研究这些差异。对64例接受机械通气的患者进行了研究：成功组（S组）有32例患者撤机成功，失败组（F组）有32例患者未能维持自主呼吸并重新连接呼吸机。使用由35个特征组成的完整集合训练的神经网络，对患者进行良好分类的准确率为64.56%。在应用特征选择程序（反向选择）后，仅使用35个特征中的8个，准确率达到了84.56%。
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