Collaborating Authors

Getting Started in the Seizure Prediction Competition: Impact, History, & Useful Resources


The currently ongoing Seizure Prediction competition--hosted by Melbourne University AES, MathWorks, and NIH--invites Kagglers to accurately forecast the occurrence of seizures using intracranial EEG recordings. This competition uniquely focuses on seizure prediction using long-term electrical brain activity from human patients obtained from the world first clinical trial of the implantable NeuroVista Seizure Advisory Sytem. In this blog post, you'll learn about the contest's potential to positively impact the lives of those who suffer from epilepsy, outcomes of previous seizure prediction contests on Kaggle, as well as resources which will help you get started in the competition including a free temporary MATLAB license and starter code. This competition is sponsored by MathWorks, the National Institutes of Health (NINDS), the American Epilepsy Society and the University of Melbourne, and organised in partnership with the Alliance for Epilepsy Research, the University of Pennsylvania and the Mayo Clinic. For many people with epilepsy, seizures reoccur at random times and greatly disrupt their cognitive and emotional state, their ability to work and drive, and their social and economic situation.

Automatic Seizure Detection in an In-Vivo Model of Epilepsy

AAAI Conferences

The goal of our research is to find patterns of EEG activity that will allow us to correctly identify seizures in living rats using machine learning techniques. Features are extracted from the EEG to characterize the signal over time. We perform model selection to reduce the set of features, as the goal is to have the algorithm running on a small personal device. The chosen features are used within a supervised classifier, based on randomized forests, in order to separate the different brain states. One of the challenges of this research is to detect all seizures, while preserving a low false positive rate, and low detection latency. We present results showing we can achieve this using data from three separate animals. The long-term goal of this research is to use this seizure detection method as part of a closed-loop adaptive neuro-stimulation device to reduce the incidence and duration of seizures.

A Wearable Chip to Predict Seizures


One of the toughest aspects of having epilepsy is not knowing when the next seizure will strike. A wearable warning system that detects pre-seizure brain activity and alerts people of its onset could alleviate some of that stress and make the disorder more manageable. To that end, IBM researchers say they have developed a portable chip that can do the job; they described their invention today in the Lancet's open access journal eBioMedicine.

How is Artificial Intelligence changing lives of epileptic patients?


Epilepsy damages lives of suffering patients and their families and if we can diagnose, predict, and prevent it, that would be a game changer for mental health. Anti-epileptic medicines have several side-effects. Imagine a world where epileptic patient had to take fewer medications or simply could have an implant which could prevent epileptic seizure. All of that would require few tiny sensors on skull, hidden underneath patients' hair and a smart phone which receives and processes the information, either offline or online, and activates a probe to prevent seizure. Well we have obviously oversimplified here but we want you to get the picture without getting lost in technical jargon first. We at Khurana group have a team working on it day and night out. While we are very proud of our efforts but there are other friends (or if you prefer to call them competitors) who are doing commendable job in the same direction for years. In this article, we would take you through what products are already out there but before that let us take you through some basics of epilepsy.

Artificial Intelligence May Speed up Epilepsy Diagnoses, Study...


A newly developed artificial intelligence (AI) system could help expedite the diagnosis of epileptic conditions such as Dravet syndrome. The AI system was described in a study, titled "A propositional AI system for supporting epilepsy diagnosis based on the 2017 epilepsy classification: Illustrated by Dravet syndrome," in the journal Epilepsy & Behavior. Epilepsy is a broad disease category for many different conditions that involve seizures. Properly diagnosing epileptic conditions can be a challenge, especially given their different causes and symptoms. For example, mutations in the SCN1A gene are the most common cause of Dravet syndrome, but not all people with Dravet syndrome have such mutations, and SCN1A mutations can also be associated with other conditions, such as febrile seizures plus.