A seizure prediction approach based on phase correlation of EEG signals achieved a prediction accuracy of 91.95% and fewer false alarms compared to state-of-the-art methods.
Automated seizure prediction has a potential in epilepsy monitoring, diagnosis, and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and prediction. This paper proposes a new seizure prediction approach based on spatiotemporal relationship of EEG signals using phase correlation. This measures the relative change between current and reference vectors of EEG signals which can be used to identify preictal/ictal (before the actual seizure onset/ actual seizure period) and interictal (period between adjacent seizures) EEG signals to predict the seizure. The experiments show that the proposed method is less sensitive to artifacts and provides higher prediction accuracy (i.e., 91.95%) and lower number of false alarms compared to the state-of-the-art methods using intracranial EEG signals in different brain locations of 21 patients from a benchmark data set.
Parvez et al. (Wed,) conducted a other in Epileptic Seizures (n=21). Phase correlation-based seizure prediction approach vs. State-of-the-art methods was evaluated on Prediction accuracy. A seizure prediction approach based on phase correlation of EEG signals achieved a prediction accuracy of 91.95% and fewer false alarms compared to state-of-the-art methods.