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Epilepsy is one of the most common neurological disorders, affecting almost 1% of the population worldwide. The categorization of seizures is usually made based on the seizure onset zone (area of the brain where the seizure initiates) the progression of the seizure and the awareness status of the patient that experience the seizure. Focal onset seizures are the most common type of seizures in adults with epilepsy. In patients with epilepsy, around 30% are not seizure free despite the use of ant-seizure medication (ASM). It is therefore paramount to accurately monitor and log seizures of the patients to improve therapeutic decisions. Nevertheless, patients will report less than 50% of their seizures. As such, under-reporting of seizures renders the seizure diary is an unreliable method in clinical practice as well as surrogate endpoint in trials for ASM. Automated electroencephalography (EEG)-based seizure detection systems are important to objectively detect and register seizures during long-term video-EEG (vEEG) recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. In this challenge the contestants are requested to train Machine Learning (ML) models to accurately detect seizure events in data obtained using a wearable device.
We propose a six weeks-long challenge on seizure detection using wearable EEG datasets obtained in UZ Leuven. Two separate tasks will be set for the participants:
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