Thanks to the crowdsourcing of more than 10,000 algorithms from around the world, epileptic seizure prediction is possible in a wider range of patients than previously thought, according to new research.
In 2016, researchers ran the Seizure Prediction Challenge on the online data science competition platform Kaggle.com. The contest focused on seizure prediction using long-term electrical brain activity recordings from humans obtained in 2013 from the world-first clinical trial of the implantable NeuroVista Seizure Advisory System.
Researchers rigorously evaluated the top algorithms and report their findings in Brain: A Journal of Neurology.
“The hope is to make seizures less like earthquakes, which can strike without warning, and more like hurricanes, where you have enough advance warning to seek safety.”
Levin Kuhlmann, from the Graeme Clarke Institute at the University of Melbourne and St. Vincent’s Hospital in Melbourne, says the contest was a huge success, with more than 646 participants, 478 teams, and more than 10,000 algorithms from around the world.
“Epilepsy affects 65 million people worldwide,” Kuhlmann says. “We wanted to draw on the intelligence from the best international data scientists to achieve advances in epileptic seizure prediction performance for patients whose seizures were the hardest to predict.”
Contestants developed algorithms to distinguish between 10-minute inter-seizure verses pre-seizure data clips and researchers tested the top algorithms on the patients with the lowest seizure prediction performance based on previous studies.
“Our evaluation revealed on average a 90 percent improvement in seizure prediction performance, compared to previous results,” Kuhlmann says.
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“Epilepsy is highly different among individuals. Results showed different algorithms performed best for different patients, supporting the use of patient-specific algorithms and long-term monitoring.”
Building on this success, researchers have developed Epilepsyecosystem.org, an online ecosystem for algorithm and data sharing to further develop and improve seizure prediction.
“Accurate seizure prediction will transform epilepsy management by offering early warnings to patients or triggering interventions,” Kuhlmann says. “Our results highlight the benefit of crowdsourcing an army of algorithms that can be trained for each patient and the best algorithm chosen for prospective, real-time seizure prediction.
“It’s about bringing together the world’s best data scientists and pooling the greatest algorithms to advance epilepsy research. The hope is to make seizures less like earthquakes, which can strike without warning, and more like hurricanes, where you have enough advance warning to seek safety.”
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Researchers from the University of Melbourne, St. Vincent’s Hospital Melbourne, Swinburne University of Technology, Mayo Clinic, the University of Pennsylvania, and Seer Medical also contributed to the work.
Source: University of Melbourne
