Necessary for seizure forecasting outcome metrics: seizure frequency and benchmark model.

ABSTRACT

Work is ongoing to advance seizure forecasting, but the performance metrics used to evaluate model effectiveness can sometimes lead to misleading outcomes. For example, some metrics improve when tested on patients with a particular range of seizure frequencies (SF). This study illustrates the connection between SF and metrics. Additionally, we compared benchmarks for testing performance a moving average (MA) or the commonly used permutation benchmark. Three data sets were used for the evaluations (1) Self-reported seizure diaries of 3,994 Seizure Tracker patients; (2) Automatically detected (and sometimes manually reported or edited) generalized tonic-clonic seizures from 2,350 Empatica Embrace 2 and Mate App seizure diary users, and (3) Simulated datasets with varying SFs. Metrics of calibration and discrimination were computed for each dataset, comparing MA and permutation performance across SF values. Most metrics were found to depend on SF. The MA model outperformed or matched the permutation model in all cases. The findings highlight SF's role in seizure forecasting accuracy and the MA model's suitability as a benchmark. This underscores the need for considering patient SF in forecasting studies and suggests the MA model may provide a better standard for evaluating future seizure forecasting models.