A novel patient-specific seizure prediction method achieved a high sensitivity of 88.34% with a false prediction rate of 0.155 h⁻¹ and an average prediction time of 22.5 minutes.
Does a patient-specific seizure prediction method based on a variational Bayesian Gaussian mixture model of zero-crossing intervals improve seizure prediction in scalp EEG compared to a random predictor?
A novel patient-specific seizure prediction method using scalp EEG zero-crossing intervals achieved high sensitivity and low false prediction rates.
A novel patient-specific seizure prediction method based on the analysis of positive zero-crossing intervals in scalp electroencephalogram (EEG) is proposed. In a moving-window analysis, the histogram of these intervals for the current EEG epoch is computed, and the values corresponding to specific bins are selected as an observation. Then, the set of observations from the last 5 min is compared with two reference sets of data points (preictal and interictal) through novel measures of similarity and dissimilarity based on a variational Bayesian Gaussian mixture model of the data. A combined index is then computed and compared with a patient-specific threshold, resulting in a cumulative measure which is utilized to form an alarm sequence for each channel. Finally, this channel-based information is used to generate a seizure prediction alarm. The proposed method was evaluated using ∼ 561 h of scalp EEG including a total of 86 seizures in 20 patients. A high sensitivity of 88.34 % was achieved with a false prediction rate of 0.155 h⁻¹ and an average prediction time of 22.5 min for the test dataset. The proposed method was also tested against a Poisson-based random predictor.
Zandi et al. (Tue,) conducted a other in Epileptic seizures (n=20). Patient-specific seizure prediction method based on a variational Bayesian Gaussian mixture model of zero-crossing intervals vs. Poisson-based random predictor was evaluated on Seizure prediction sensitivity. A novel patient-specific seizure prediction method achieved a high sensitivity of 88.34% with a false prediction rate of 0.155 h⁻¹ and an average prediction time of 22.5 minutes.
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