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Repeated epileptic seizures impair around 65 million people worldwide and asuccessful prediction of seizures could significantly help patients sufferingfrom refractory epilepsy. For two dogs with yearlong intracranialelectroencephalography (iEEG) recordings, we studied the influence of timeseries nonstationarity on the performance of seizure prediction using in-housedeveloped machine learning algorithms. We observed a long-term evolution on thescale of weeks or months in iEEG time series that may be represented asswitching between certain meta-states. To better predict impending seizures,retraining of prediction algorithms is therefore necessary and the retrainingschedule should be adjusted to the change in meta-states. There is evidencethat the nature of seizure-free interictal clips also changes with thetransition between meta-states, accwhich has been shown relevant for seizureprediction.

Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?