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