OBJECTIVES: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.
OBJECTIVES: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.
Authors: Marcos Martínez-Romero; José M Vázquez-Naya; Javier Pereira; Miguel Pereira; Alejandro Pazos; Gerardo Baños Journal: Comput Math Methods Med Date: 2013-02-16 Impact factor: 2.238