Philippa J. Karoly

Objective: Epileptic seizures occurring in cyclical patterns is increasingly recognized as a significant opportunity to advance epilepsy management. Current methods for detecting seizure cycles rely on intrusive techniques or specialized biomarkers, thereby limiting their accessibility. This study evaluates a non-invasive seizure cycle detection method using seizure diaries and compares its accuracy with cycles identified from intracranial electroencephalography (iEEG) seizures and interictal epileptiform discharges (IEDs). Methods: Using data from a previously published first in-human iEEG device trial (n = 10), we analyzed seizure cycles identified through diary reports, iEEG seizures, and IEDs. Cycle similarities across diary reports, iEEG seizures, and IEDs were evaluated at periods of 1 to 45 days using spectral coherence, accuracy, precision, recall, and the false-positive rate. Results: A spectral coherence analysis of the raw signals showed moderately similar periodic components between diary seizures/day and iEEG seizures/day (median = .43, IQR = .68). In contrast, there was low coherence between diary seizures/day and IEDs/day (median = .11, IQR = .18) and iEEG seizures/day and IEDs/day (median = .12, IQR = .19). Accuracy, precision, recall scores, and false-positive rates of iEEG seizure cycles from diary seizure cycles were significantly higher than chance across all participants (accuracy (mean ± standard deviation): .95 ± .02; precision: .56 ± .19; recall: .56 ± .19; false-positive rate: .02 ± .01). However, accuracy, precision, and recall scores of IED cycles from both diary and iEEG cycles did not perform above chance, on average. Recall scores were compared across good diary reporters, under-reporters, and over-reporters, with recall scores generally performing better in good reporters and under-reporters compared to over-reporters. Conclusions: These findings suggest that iEEG seizure cycles can be identified with diary reports, even in individuals who under- and over-report seizures. This approach offers an accessible alternative for monitoring seizure cycles compared to more invasive methods.

Objective: Seizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of life for those with epilepsy but must be practical for long-term use. This study presents the first validation of a seizure-forecasting system using ultra-long-term, non-invasive wearable data. Methods: Eleven participants with epilepsy were recruited for continuous monitoring, capturing heart rate and step count via wrist-worn devices and seizures via electroencephalography (average recording duration of 337 days). Two hybrid models-combining machine learning and cycle-based methods-were proposed to forecast seizures at both short (minutes) and long (up to 44 days) horizons. Results: The Seizure Warning System (SWS), designed for forecasting near-term seizures, and the Seizure Risk System (SRS), designed for forecasting long-term risk, both outperformed traditional models. In addition, the SRS reduced high-risk time by 29% while increasing sensitivity by 11%. Conclusions: These improvements mark a significant advancement in making seizure forecasting more practical and effective.

Background: Multiday cyclic patterns underlying the timing of seizures are well-established in adults with epilepsy. However, longer-term patterns underpinning these models are yet to be explored extensively in pediatric cohorts. This study aims to identify and compare multiday seizure cycles between pediatric and adult cohorts, followed by a preliminary validation of cycle-based methods for estimating seizure likelihood in a pediatric cohort. Methods: Multiday seizure cycles were extracted retrospectively from 325 (71 pediatric) electronic seizure diary users with confirmed epilepsy. Cycles were grouped (k-means clustering) and seizure cycles quantified (synchronisation index) with significant cycles identified (Rayleigh test (p < 0.05)). Wilcoxon rank-sum test assessed differences in prevalence and strength of cycle groups between pediatric and adult cohorts. The accuracy of cycle-based models to track pediatric seizure occurrence was calculated from the receiver operating characteristic (area under the curve; AUC) comparing estimated cycles to shuffled surrogate data and further validated with a moving average model. Results: 30,019 seizures (pediatric: Median = 51, IQR (Q1 = 30, Q3 = 115), Range (21-661), adult: Median = 46, IQR (Q1 = 31, Q3 = 93), Range (20-1112) were analysed and seizure cycles grouped across circadian (0.5-1.5 days), about-weekly (2-12 days), about-fortnightly (13-22 days) and about-monthly (23-32 days) periodicities. Significant cycles were identified in each cycle group, with no differences in prevalence or cycle strength between pediatric and adult cohorts. Estimated cycles showed a reliable assessment of observed seizure occurrence (significantly (p < 0.05) better performance compared to random models for 88% (44 of 50) and moving average models for 50% (25 of 50) of observed daily seizure occurrence). Conclusions: Multiday seizure cycles estimated from seizure diaries present a viable model for identifying longer-term seizure patterns in a pediatric cohort. Knowledge of these individual seizure cycles has potential to reduce the unpredictability of seizure timing and inform clinical decision-making.

Objective.Epilepsy affects millions globally, with a significant subset of patients suffering from drug-resistant focal seizures. Understanding the underlying neurodynamics of seizure initiation and propagation is crucial for advancing treatment and diagnostics. In this study, we present a novel, inference-based approach for analyzing the temporal evolution of cortical stability and chaos during focal epileptic seizures.Approach.Utilizing a multi-region neural mass model, we estimate time-varying synaptic connectivity from intracranial electroencephalography (iEEG) data collected from individuals with drug-resistant focal epilepsy.Main results.Our analysis reveals distinct preictal and ictal phases characterized by shifts in cortical stability, heightened chaos in the ictal phase, and highlight the critical role of inter-regional communication in driving chaotic cortical behaviour. We demonstrate that cortical dynamics are consistently destabilized prior to seizure onset, with a transient reduction in instability at seizure onset, followed by a significant increase throughout the seizure.Significance.This work provides new insights into the mechanisms of seizure generation and offers potential biomarkers for predicting seizure events. Our findings pave the way for innovative therapeutic strategies targeting cortical stability and chaos to manage epilepsy.

Objective: A significant challenge of video-electroencephalography (vEEG) in epilepsy diagnosis is timing monitoring sessions to capture epileptiform activity. In this study, we introduce and validate "pro-ictal EEG scheduling", a method to schedule vEEG monitoring to coincide with periods of increased seizure likelihood as a low-risk approach to enhance the diagnostic yield. Methods: A database of long-term ambulatory vEEG monitoring sessions (n = 5,038) of adults and children was examined. Data from linked electronic seizure diaries were extracted (minimum 10 self-reported events) to generate cycle-based estimates of seizure risk. In adults, vEEG monitoring sessions coinciding with periods of estimated high-risk were allocated to the high-risk group (n = 305) and compared to remaining studies (baseline: n = 3,586). Test of proportions and risk-ratios (RR) were applied to index differences in proportions and likelihood of capturing outcome measures (abnormal report, confirmed seizure, and diary event) during monitoring. The impact of clinical and demographic factors (age, sex, epilepsy-type, and medication) was also explored. Results: During vEEG monitoring, the high-risk group was significantly more likely to have an abnormal vEEG report (190/305:62% vs 1,790/3,586:50% [%change = 12%], RR = 1.25, 95% confidence interval [CI] = [1.137-1.370], p < 0.001), present with a confirmed seizure (56/305:18% vs 424/3,586:11% [%change = 7%], RR = 1.63, 95% CI = [1.265-2.101], p < 0.001) and report an event (153/305:50% vs 1,267/3,586:35% (%change = 15%), RR = 1.420, 95% CI = [1.259:1.602], p < 0.001). Similar effects were observed across clinical and demographic features. Conclusions: This study provides the first large-scale validation of pro-ictal EEG scheduling in improving the yield of vEEG. This innovative approach offers a pragmatic and low-risk strategy to enhance the diagnostic capabilities of vEEG monitoring, significantly impacting epilepsy management. ANN NEUROL 2024;96:1148-1159.