Seizure Cycle app: A feasibility study.

Area of Science:

• Neurology
• Epilepsy Research
• Digital Health

Background:

• Catamenial seizure exacerbation (CSE) is difficult to monitor accurately due to inconsistent patient self-reporting.
• Improved methods for recognizing CSE are crucial for effective patient management.
• The Seizure Cycle app was investigated as a potential tool for tracking seizure patterns related to the menstrual cycle.

Purpose of the Study:

• To assess the feasibility of using the Seizure Cycle app for identifying catamenial seizure exacerbation (CSE).
• To evaluate the app's utility in collecting seizure and menstrual cycle data for CSE diagnosis.

Main Methods:

• Participants (n=8) logged menstrual cycle and seizure data via the Seizure Cycle app for up to 6 months.
• CSE was defined using established criteria based on seizure frequency during specific cycle phases (menstrual, ovulatory, luteal).
• Feasibility was determined by the proportion of participants completing 4- and 6-month data logs.

Main Results:

• 50% of participants completed 6 months of data logging, indicating moderate feasibility.
• CSE (Type C3, anovulatory cycles) was potentially identified in one participant based on seizure counts.
• The use of average daily seizure frequency (ADSF) did not confirm the CSE classification in this case.

Conclusions:

• The Seizure Cycle app demonstrates feasibility as a tool to aid in the diagnosis of underrecognized CSE.
• While preliminary identification of CSE occurred, clearer definitions for certain patterns (e.g., C3) are needed.
• Future app automation and larger datasets are essential for robust CSE assessment and treatment development.