Epilepsia. Official Journal of the International League Against Epilepsy

Abstract

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation of data rather than through explicit instructions. Alongside widespread use in image recognition, language processing, and data mining, machine learning techniques have received increasing attention in medical applications, ranging from automated imaging analysis to disease forecasting. This review examines the parallel progress made in epilepsy, highlighting applications in automated seizure detection from electroencephalography (EEG), video, and kinetic data, automated imaging analysis and pre‐surgical ...

Abstract

Objective

Considering the complexity of neuronal circuits and their epilepsy‐associated alterations, epilepsy models cannot be completely replaced by in vitro experimental approaches. Decisions about ethical approval of in vivo studies require a thorough weighing of the animal’s burden and the benefit regarding the expected gain in knowledge.

Methods

Based on combined behavioral, biochemical, and physiological analyses, we assessed the impact on animal well‐being and condition in different phases of the pilocarpine post–status epilepticus (SE) model in rats.

Results

As a consequence of SE, increased levels ...

Abstract

Individual seizure rates are highly volatile, with large fluctuations from month‐to‐month. Nevertheless, changes in individual mean seizure rates are used to measure whether or not trial participants successfully respond to treatment. This study aims to quantify the challenges in identifying individual treatment responders in epilepsy. A power calculation was performed to determine the trial duration required to detect a significant 50% decrease in seizure rates (P <  .05) for individuals. Seizure rate simulations were also performed to determine the number of people ...