Si Y, Gotman J, Pasupathy A, Flanagan D, Rosenblatt B, Gottesman R
The Montreal Neurological Institute, McGill University, Quebec, Canada.
Electroencephalogr Clin Neurophysiol. 1998 Jun;106(6):488-500. doi: 10.1016/s0013-4694(97)00154-5.
was to design a warning system for the pediatric intensive care unit (PICU). The system should be able to make statements at regular intervals about the level of abnormality of the EEG. The warnings are aimed at alerting an expert that the EEG may be abnormal and needs to be examined.
A total of 188 EEG sections lasting 6 h each were obtained from 74 patients in the PICU. Features were extracted from these EEGs, and with the use of fuzzy logic and neural networks, we designed an expert system capable of imitating a trained EEGer in providing an overall judgment of abnormality about the EEG. The 188 sections were used in training and testing the system using the rotation method, thus separating training and testing data.
The EEGer and the expert system classified the EEGs in 7 levels of abnormality. There was concordance between the two in 45% of cases. The expert system was within one abnormality level of the EEGer in 91% of cases and within two levels in 97%.
We were therefore able to design a system capable of providing reliably an assessment of the level of abnormality of a 6 h section of EEG. This system was validated with a large data set, and could prove useful as a warning device during long-term ICU monitoring to alert a neurophysiologist that an EEG requires attention.
设计一种用于儿科重症监护病房（PICU）的预警系统。该系统应能够定期对脑电图（EEG）的异常程度进行评估。这些预警旨在提醒专家EEG可能异常，需要进行检查。
从PICU的74名患者中获取了总共188段时长均为6小时的EEG。从这些EEG中提取特征，并利用模糊逻辑和神经网络，我们设计了一个专家系统，该系统能够模仿训练有素的脑电图专家，对EEG的异常情况提供总体判断。使用轮换法将这188段EEG用于系统的训练和测试，从而分离训练数据和测试数据。
脑电图专家和专家系统将EEG分为7个异常等级。两者在45%的病例中判断一致。在91%的病例中，专家系统的判断与脑电图专家的判断相差不超过一个异常等级，在97%的病例中相差不超过两个等级。
因此，我们能够设计出一个系统，该系统能够可靠地评估一段6小时EEG的异常程度。该系统通过大量数据集进行了验证，在长期重症监护病房监测期间，作为一种预警设备，可提醒神经生理学家某段EEG需要关注，可能会很有用。
