Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and EEG-confirmed seizures in ambulatory patients with epilepsy in their natural home environments.
Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC).
Fifty-four subjects returned 10,269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% CI = [1.12, 3.61], AUC = 0.61, p = 0.02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (5.37, [3.53, 8.16], 0.76, < 0.001) were most strongly associated with future self-reported seizures and high perceived seizure risk (3.34, [1.87, 5.95], 0.69, < 0.001) remained significant when prior self-reported seizures were added to the model. No correlation with medical adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures.
Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
Jie Cui, Irena Balzekas, Ewan Nurse, Pedro Viana, Nicholas Gregg, Philippa Karoly, Rachel E. Stirling, Gregory Worrell, Mark P. Richardson, Dean R. Freestone, and Benjamin H. Brinkmann.
Published on 12 June 2023