ArticleEpilepsia2023
Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.
Article in Epilepsia, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Seizure detection using ultra-long-term subcutaneous electroencephalography: A deep learning CNN-BiLSTM approach.Epilepsia · 2026Article
- Seizure burden and treatment efficacy evaluation in anti-LGI1 autoimmune encephalitis patients using wearable device: a pilot feasibility study.BMJ neurology open · 2026Article
- Rhythms in Longitudinal Thalamic Recordings are Linked to Seizure Risk.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Assessing the feasibility of digital keypress statistics to detect seizures and capture cognitive impairment in patients with epilepsy: A pilot study.Epilepsy & behavior : E&B · 2024Article
- How accurate do self-reported seizures need to be for effective medication management in epilepsy?Epilepsia · 2024Article
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11 authors.
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Abstract
objectivePrevious 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 electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments.
methodsLong-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).
resultsFifty-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% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. SIGNIFICANCE: 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.
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