UNA-Europa epilepsy challenge
The Seizure Detection Challenge, in collaboration with Una Europa, aims at developing innovative and robust machine learning (ML) frameworks for electroencephalography (EEG) data processing, in which the end use case is detection of epileptic seizures.
In patients with epilepsy, lifelong treatment with antiseizure medications may be required, as relapse after withdrawal is high and even patients who are considered to be seizure free under treatment, can still relapse. It is, therefore, paramount to accurately detect seizures to improve therapeutic decisions. However, the subtle clinical manifestation of seizures hampers the patient’s and the caregiver’s ability to correctly report the seizure in the seizure diary, as serious underreporting of seizures in self diaries has been reported. As such, the seizure diary is an unreliable method, although commonly used in clinical practice as well as surrogate endpoint in trials for antiseizure medication. Automated EEG-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG 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. A wearable device that records EEG with behind-the-ear (bhe) electrodes, the SensorDot (SD) of Byteflies, has been developed during SeizeIT study. This CE-marked device is a user-friendly wearable that makes use of two behind-the-ear channels to detect seizures.
Within the scope of this challenge, participants will be presented with a task focused on training ML models to accurately detect seizure events in data obtained from the SD wearable device.
Practical information:
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In patients with epilepsy, lifelong treatment with antiseizure medications may be required, as relapse after withdrawal is high and even patients who are considered to be seizure free under treatment, can still relapse. It is, therefore, paramount to accurately detect seizures to improve therapeutic decisions. However, the subtle clinical manifestation of seizures hampers the patient’s and the caregiver’s ability to correctly report the seizure in the seizure diary, as serious underreporting of seizures in self diaries has been reported. As such, the seizure diary is an unreliable method, although commonly used in clinical practice as well as surrogate endpoint in trials for antiseizure medication. Automated EEG-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG 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. A wearable device that records EEG with behind-the-ear (bhe) electrodes, the SensorDot (SD) of Byteflies, has been developed during SeizeIT study. This CE-marked device is a user-friendly wearable that makes use of two behind-the-ear channels to detect seizures.
Within the scope of this challenge, participants will be presented with a task focused on training ML models to accurately detect seizure events in data obtained from the SD wearable device.
Teachers / speakers
Prof. dr. Maarten De Vos is hoogleraar aan de faculteiten Ingenieurswetenschappen en Geneeskunde van KU Leuven. Hij richt zich op het verbeteren van data science-benaderingen voor verschillende toepassingen in de gezondheidszorg. Zijn AI-oplossingen worden gebruikt op verschillende ziekenhuisafdelingen, variërend van neonatologie tot ouderenzorg.
I am Christos Chatzichristos, currently a post-doctoral researcher at KU Leuven. My educational background revolves around electrical and computer engineering, with a specialization in Biomedical Applications and an emphasis on signal processing during both my Master's and Ph.D. studies. During my doctoral journey, I witnessed the profound impact of neural networks on the field of signal processing, marking the inception of my foray into the realm of AI applications. I hold a strong belief in fostering broad interdisciplinary collaborations, as I believe that research today cannot thrive in isolation within a single domain. Artificial intelligence stands as a potent tool to expedite healthcare research. However, to truly harness its potential, we must bridge the gap by facilitating healthcare professionals' understanding of fundamental AI concepts, just as they aid biomedical engineers in unraveling the mysteries of the human body. So, here's to using AI to accelerate healthcare research while ensuring that we all speak the same language – whether it's the language of algorithms or the language of anatomy!
