A neural network based Markov model of EEG hidden dynamic

被引:0
作者
Silipo, R [1 ]
Deco, G [1 ]
Bartsch, H [1 ]
机构
[1] Int Comp Sci Inst, ICSI, Berkeley, CA 94704 USA
来源
PROCEEDING OF THE THIRD INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND EXPERT SYSTEMS IN MEDICINE AND HEALTHCARE | 1998年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The hidden dynamic of the couple (F3, F4) of EEG leeds is modeled by means of nonlinear Markov models, in order to investigate possible differences in case of meningeoma, malignant glioma, and intact brain. The conditional probabilities of the transition states of the Markov models are represented as sums of Gaussian distributions, whose parameters are estimated by means of multilayered Perceptrons. The dynamic of the pair (F3, F4) is tested against a hierarchy of null hypotheses corresponding to nonlinear Markov processes of increasing order. The minimum accepted order gives an indication of the organization degree of the hidden dynamic of the signal. A structured dynamic is detected in both leads (F3, F4) of normal EEGs, confirming the very complex structure of the underlying system. Different correlations between the two hemispheres activities seem to discriminate meningeoma, malignant glioma, and no pathological status. Moreover loss of structure can represent a good hint for glioma/meningeoma localization.
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页码:58 / 66
页数:9
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