Researchers using algorithms to predict epileptic seizures
Predicting epileptic seizures is closer to becoming a reality, thanks to the crowdsourcing of thousands of algorithms worldwide. According to a study by University of Melbourne researchers, clinically relevant seizure predictions are now possible after researchers collected more than 10,000 algorithms during a contest in 2016.
The contest featured more than 646 participants and 478 teams submitting more than 10,000 algorithms. After researchers evaluated the top algorithms, findings were published in Brain: A Journal of Neurology.
“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,” a press release stated.
“Contestants developed algorithms to distinguish between 10-minute inter-seizure versus pre-seizure data clips, and the top algorithms were tested on the patients with the lowest seizure prediction performance based on previous studies.”
Levin Kuhlmann, PhD, with Australia's University of Melbourne, said the team’s evaluation revealed, on average, a 90 percent improvement in seizure prediction performance compared to previous results. Since the contest, researchers have developed the site Epilepsyecosystem.org, an 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 said in the release. “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.”
Danielle covers Clinical Innovation & Technology as a senior news writer for TriMed Media. Previously, she worked as a news reporter in northeast Missouri and earned a journalism degree from the University of Illinois at Urbana-Champaign. She's also a huge fan of the Chicago Cubs, Bears and Bulls.