Epilepsy


Machine Learning Treats Brain Disorders Where They Most Often Occur

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These neuropsychiatric disorders are prevalent in low- to middle-income countries due to various factors, e.g. Around 80 percent of the world's epilepsy occurs in low- to middle-income countries, but only 20 percent of people get treatment. The physician-to-patient ratio can be as low as one for every 20,000 people in those countries, with even fewer psychiatrists and neurologists, causing a so-called treatment gap.1 However, timely diagnosis and treatment of epilepsy is possible and can make a difference.2 Last fall, partnering with the Nanyang Technological University (NTU) of Singapore, we took the first steps in tackling this challenge in our Science for Social Good program. Our team included a Social Good Fellow from Columbia University, several machine learning and cloud computing researchers from IBM Research, and collaborators from NTU. Together, we came up with a cloud-based automated machine learning approach to provide decision support for non-specialist physicians in electroencephalography (EEG) analysis and interpretation.


A.I. spots epilepsy seizures in advance - Futurity

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You are free to share this article under the Attribution 4.0 International license. Researchers report that they've used a mobile, brain-inspired processor to analyze brain signals from retrospective patient data and successfully predict an average of 69 percent of seizures across all patients with artificial intelligence. The research could help pave the way for personalized seizure prediction for patients with epilepsy. "Our algorithm also allows for instantaneous and easy adjustment, giving patients the flexibility to control how sensitive and in advance the warning is…" With a third of epilepsy patients worldwide currently living with unpredictable seizures that are not adequately controlled through medication or otherwise. This research could dramatically improve the lives of 250,000 Australians and 65 million people worldwide, says Mark Cook, director of the University of Melbourne's Graeme Clark Institute for Biomedical Engineering and director of neurology at St. Vincent's Hospital in Melbourne.



AI Just Learned How to Boost the Brain's Memory

WIRED

When it comes to black boxes, there is none more black than the human brain. Our gray matter is so complex, scientists lament, that it can't quite understand itself.


AI Just Learned How to Boost the Brain's Memory

#artificialintelligence

When it comes to black boxes, there is none more black than the human brain. Our gray matter is so complex, scientists lament, that it can't quite understand itself.


How is Artificial Intelligence changing lives of epileptic patients?

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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.


A Wearable Chip to Predict Seizures

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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.


IBM and University of Melbourne present seizure prediction system

ZDNet

Researchers from IBM and the University of Melbourne have developed a proof-of-concept seizure forecasting system that predicted an average of 69 percent of seizures across 10 epilepsy patients in a dataset.


Scientists Are Using Machine Learning To Better Predict Epilepsy

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There are 2 aspects of this research that are worth highlighting: (1) we showed that micro-structural extra-hippocampal abnormalities are consistent enough across medial temporal lobe epilepsy (TLE) patients that they can be used to predict TLE, and (2) we obtained regularization values for the models trained on this sparse data in an unusual but effective manner.


Clonal analysis of newborn hippocampal dentate granule cell proliferation and development in temporal lobe epilepsy

arXiv.org Machine Learning

Hippocampal dentate granule cells are among the few neuronal cell types generated throughout adult life in mammals. In the normal brain, new granule cells are generated from progenitors in the subgranular zone and integrate in a typical fashion. During the development of epilepsy, granule cell integration is profoundly altered. The new cells migrate to ectopic locations and develop misoriented basal dendrites. Although it has been established that these abnormal cells are newly generated, it is not known whether they arise ubiquitously throughout the progenitor cell pool or are derived from a smaller number of bad actor progenitors. To explore this question, we conducted a clonal analysis study in mice expressing the Brainbow fluorescent protein reporter construct in dentate granule cell progenitors. Mice were examined 2 months after pilocarpine-induced status epilepticus, a treatment that leads to the development of epilepsy. Brain sections were rendered translucent so that entire hippocampi could be reconstructed and all fluorescently labeled cells identified. Our findings reveal that a small number of progenitors produce the majority of ectopic cells following status epilepticus, indicating that either the affected progenitors or their local microenvironments have become pathological. By contrast, granule cells with basal dendrites were equally distributed among clonal groups. This indicates that these progenitors can produce normal cells and suggests that global factors sporadically disrupt the dendritic development of some new cells. Together, these findings strongly predict that distinct mechanisms regulate different aspects