Gaussian Process Modelling as an Indicator of Neonatal Seizure

S. Faul, G. Gregorčič, G. Boylan, W. Marnane, G. Lightbody, and S. Connolly (Ireland)


Gaussian process modelling, neonatal seizure detection


Gaussian process models have some attractive advantages over parametric models and neural networks. They have a small number of tunable parameters, give a measure of the uncertainty of the model prediction, and obtain a rel atively good model when only a small set of training data is available. In this study the theory of Gaussian process models has been applied to the neonatal seizure detection problem. Two measures are calculated from 1 second win dows of EEG recordings; the variance (certainty) of a one step ahead prediction, and the ratio of the first model hy perparameter to the last. In ANOVA tests both measures show statistical difference in their values for non-seizure and seizure EEG. A comparison with a similar Autoregres sive (AR) modelling approach shows that Gaussian Process model methods show great promise in real-time neonatal seizure detection.

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