Epilepsy


EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms

Neural Information Processing Systems

This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC (0.72, 0.62, 0.69) compared to sampling-based alternatives.


Deep Learning Models for Automatic Seizure Detection in Epilepsy

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Cleveland Clinic is a non-profit academic medical center. Advertising on our site helps support our mission. Epilepsy is the second most common neurological disorder, impacting 1% to 2% of the world's population. Individuals with epilepsy typically undergo long-term monitoring of the brain's electrical activity with EEG recordings for several days. The recorded EEG data are manually reviewed by a trained neurologist, a neurophysiologist or a skilled EEG reader to identify epileptic seizures or interictal discharges that characterize the individual's epilepsy.


Exploring the Characterization and Classification of EEG Signals for a Computer-Aided Epilepsy Diagnosis System

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Epilepsy occurs when localized electrical activity of neurons suffer from an imbalance. One of the most adequate methods for diagnosing and monitoring is via the analysis of electroencephalographic (EEG) signals. Despite there is a wide range of alternatives to characterize and classify EEG signals for epilepsy analysis purposes, many key aspects related to accuracy and physiological interpretation are still considered as open issues. In this paper, this work performs an exploratory study in order to identify the most adequate frequently-used methods for characterizing and classifying epileptic seizures. In this regard, a comparative study is carried out on several subsets of features using four representative classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM).


Digital Health Trial Uses AI For Better Epilepsy Treatment Decisions

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Imagine having to choose from over 14,000 different treatment scenarios to decide which drugs might be best for a child or a loved one affected by epilepsy. This is what faces many families according to the experts at Stanford and doc.ai who have announced a new type of clinical trial using artificial intelligence (AI). The project's goal is to help make the process more scientific using population data and less prone to lengthy individual trial-and-error. Researchers are analyzing medications, side effects, genomic information, environmental exposures, activity and even physical traits. This type of work produces vast amounts of information and requires so much processing power that it can only be performed by the latest AI systems.


Researchers Develop AI That Can Predict Seizures Before They Happen

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Previously, research groups were able to analyze brain activity using electroencephalogram (EEG) tests from which they could use the data to develop predictive models. I was with a friend who had a seizure, and it was incredibly scary. We were sitting at a bar in Brooklyn watching a Mets game, nothing out of the ordinary, when suddenly he just stood up and fell backwards, knocked his head into a chair and went into convulsions. I had no idea he suffered from periodic epileptic seizures. And it's much, much more horrible if you're the one who suffers from seizures.


A New AI System Could Create More Hope For People With Epilepsy

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Recently, a team of researchers from the MIT-IBM Watson AI Lab created a method of displaying what a Generative Adversarial Network leaves out of an image when asked to generate images. The study was dubbed Seeing What a GAN Cannot Generate, and it was recently presented at the International Conference on Computer Vision. Generative Adversarial Networks have become more robust, sophisticated, and widely used in the past few years. They've become quite good at rendering images full of detail, as long as that image is confined to a relatively small area. However, when GANs are used to generate images of larger scenes and environments, they tend not to perform as well.


Epileptic Seizure Prediction becomes much easier with the new Artificial Intelligence technology

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Recently, Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette have introduced a completely new Artificial Intelligence (AI) system that predicts epilepsy seizures. According to the World Health Organization's reports, around 50 million people around the world are suffering from epilepsy and 70% of those patients can control the seizures through medications. The new AI technology shows 99.6% accurate results, and the best thing about it is that it predicts the attacks an hour before it happens. In this way, the patient can gear up for it and take medications that can prevent its occurrence. Having enough time to control the attack is what a patient needs.


Researchers develop an AI system with near-perfect seizure prediction

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While it's not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure. It'll still be some time before this technique will be available for widespread use -- the team is now working on a custom chip that can help process the necessary algorithms -- but it could be life-changing news for patients with epilepsy.


Researchers develop an AI system with near-perfect seizure prediction

#artificialintelligence

While it's not a complete fix, the new AI system, developed by Hisham Daoud and Magdy Bayoumi of the University of Louisiana at Lafayette, is a major leap forward from existing prediction methods. Currently, other methods analyze brain activity with an EEG (electroencephalogram) test and apply a predictive model afterwards. The new method does both of those things at once, with the help of a deep learning algorithm that maps brain activity and another that can predict the electrical channels lighting up during a seizure. It'll still be some time before this technique will be available for widespread use -- the team is now working on a custom chip that can help process the necessary algorithms -- but it could be life-changing news for patients with epilepsy.