A Bayesian switching linear dynamical system for estimating seizure chronotypes

Female sex steroids and epilepsy: Part 2. A practical and human focus on catamenial epilepsy

Catamenial epilepsy is the best described and most researched sex steroid-specific seizure exacerbation. Yet despite this there are no current evidence-based treatments, nor an accepted diagnostic tool. The best tool we currently have is tracking seizures over menstrual cycles; however, the reality of tracking seizures and menstrual cycles is fraught with challenges. In Part 1 of this two-part review, we outlined the often complex and reciprocal relationship between seizures and sex steroids. An adaptable means of tracking is required. In this review, we outline the extent and limitations of current knowledge on catamenial epilepsy. We use sample data to show how seizure exacerbations can be tracked in short/long and even irregular menstrual cycles. We describe how seizure severity, an often overlooked and underresearched form of catamenial seizure exacerbation, can also be tracked. Finally, given the lack of treatment options for females profoundly affected by catamenial epilepsy, Section 3 focuses on current methods and models for researching sex steroids and seizures as well as limitations and future directions. To permit more informative, mechanism-focused research in humans, the need for both a consistent classification of catamenial epilepsy and an objective biomarker is highlighted.

Forecasting seizure likelihood from cycles of self-reported events and heart rate: a prospective pilot study

BACKGROUND

Seizure risk forecasting could reduce injuries and even deaths in people with epilepsy. There is great interest in using non-invasive wearable devices to generate forecasts of seizure risk. Forecasts based on cycles of epileptic activity, seizure times or heart rate have provided promising forecasting results. This study validates a forecasting method using multimodal cycles recorded from wearable devices.

METHOD

Seizure and heart rate cycles were extracted from 13 participants. The mean period of heart rate data from a smartwatch was 562 days, with a mean of 125 self-reported seizures from a smartphone app. The relationship between seizure onset time and phases of seizure and heart rate cycles was investigated. An additive regression model was used to project heart rate cycles. The results of forecasts using seizure cycles, heart rate cycles, and a combination of both were compared. Forecasting performance was evaluated in 6 of 13 participants in a prospective setting, using long-term data collected after algorithms were developed.

FINDINGS

The results showed that the best forecasts achieved a mean area under the receiver-operating characteristic curve (AUC) of 0.73 for 9/13 participants showing performance above chance during retrospective validation. Subject-specific forecasts evaluated with prospective data showed a mean AUC of 0.77 with 4/6 participants showing performance above chance.

INTERPRETATION

The results of this study demonstrate that cycles detected from multimodal data can be combined within a single, scalable seizure risk forecasting algorithm to provide robust performance. The presented forecasting method enabled seizure risk to be estimated for an arbitrary future period and could be generalised across a range of data types. In contrast to earlier work, the current study evaluated forecasts prospectively, in subjects blinded to their seizure risk outputs, representing a critical step towards clinical applications.

FUNDING

This study was funded by an Australian Government National Health & Medical Research Council and BioMedTech Horizons grant. The study also received support from the Epilepsy Foundation of America's 'My Seizure Gauge' grant.