Prospective Evaluation of Probabilistic Predictions of Epileptic Seizure Risk Using the EPIDAY Tool

Epilepsy

epilepsy, Neurology, algorithm

Studies suggest the existence of a pre-critical state preceding the onset of an epileptic seizure. Identifying these states from self-reported prodromal symptoms, combined with machine learning algorithms, could help anticipate seizures.

Around 65 million people worldwide, or 1% of the global population, suffer from epilepsy. It is the 3rd most common neurological pathology. Epilepsy is a chronic condition liable to generate spontaneous and repeated epileptic seizures, and it is estimated that around a third of patients are drug-resistant and will continue to have seizures despite appropriate anti-epileptic treatment. The onset of a seizure is a paroxysmal and unpredictable phenomenon - a thunderclap in a serene sky - which accounts for the handicap and social repercussions for patients. The concept of a limited two-state model in epilepsy - i.e. intercritical/critical - has been challenged in recent decades. Ictogenesis could include a transitional state characterized by changes in cortical excitability that would pave the way for the onset of an epileptic seizure. This so-called pre-critical state is the scientific basis for seizure prediction models. If this state can be detected long enough before the onset of a seizure to detect a change in the brain's state, a seizure-stopping intervention (medication, biofeedback techniques, stimulation techniques, etc.), or at least safety measures, can be proposed. While a deterministic approach has long been applied to predictive models - to predict the occurrence of the next crisis - a new strategy has more recently developed. Today's strategies are more realistic and adapted to non-linear dynamic systems. Indeed, probabilistic approaches from the meteorological sciences are increasingly being applied to crisis prediction models. The aim of crisis forecasting is to estimate the probability of a future crisis at any given time, whereas classical prediction algorithms aim to accurately predict the occurrence of a future crisis. In this way, we can identify a pro-critical state, i.e. a state at high risk of epileptic seizure. Several studies have suggested the existence of a pre-critical period. However, identifying specific pre-critical biomarkers remains a major challenge. While information derived from EEG signals has long been favored, analysis of clinical symptoms has emerged more recently. Pre-critical clinical symptoms, otherwise known as prodromes or prodromal symptoms, may precede the seizure by several hours. Some studies have also highlighted the value of integrating self-prediction - the patient's subjective assessment of the risk of an upcoming crisis - without anticipation models. Previous work by the investigators has developed a classification algorithm capable of identifying a pre-critical state from the daily assessment of several prodromal symptoms. These results were obtained in a hospital setting, with good classification performance. This work was the subject of a European patent application (No. 20306548.7) on December 11, 2020 and an international patent application (No. PCT/EP2021/085146) on December 10, 2021: A computer-implemented model for predicting occurrence of a seizure and training method thereof. The main hypothesis of this study is that a machine learning algorithm based on the daily assessment of prodromal symptoms could identify seizure-prone states in patients with epilepsy.

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