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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