Seizure prediction with long-term iEEG recordings: What can we learn from data nonstationarity?
Hongliu Yang, Matthias Eberlein, Jens Müller, Ronald Tetzlaff · 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · 2021
Repeated epileptic seizures impair around 65 million people worldwide and a successful prediction of seizures could significantly h elp p atients suffering from refractory epilepsy. For two dogs with yearlong intracranial electroencephalography (iEEG) recordings, we studied the influence of time series nonstationarity on the performance of seizure prediction using in-house developed machine learning algorithms. We observed a long-term evolution on the scale of weeks or months in iEEG time series that may be represented as switching between certain meta-states. To better predict impending seizures, retraining of prediction algorithms is therefore necessary and the retraining schedule should be adjusted to the change in meta-states. There is evidence that the nature of seizure-free interictal clips also changes with the transition between meta-states, which has been shown relevant for seizure prediction.