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    Home > Active Ingredient News > Study of Nervous System > Ann Neurol-Harvard University New Discovery: Artificial Intelligence, Helping Epilepsy Diagnosis and Treatment

    Ann Neurol-Harvard University New Discovery: Artificial Intelligence, Helping Epilepsy Diagnosis and Treatment

    • Last Update: 2021-08-03
    • Source: Internet
    • Author: User
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    In critically ill patients undergoing electroencephalography (EEG) monitoring, as many as half of them haveEpilepsy and epilepsy-like periodic and rhythmic patterns of brain activity ("epilepsy-like activity")


    epilepsy

    However, the prognostic relevance of the burden of epileptiform activity (EA) has not been quantified in large and heterogeneous cohorts that span the entire nervous system, medical, and surgical diseases


    In this way, Sahar F.


    Tatian conducted a single-center retrospective analysis of 1967 neurology, internal medicine, and surgery patients who received >16 hours of continuous EEG between 2011 and 2017


    They assessed:



    , Peak burden of epilepsy is independently related to adverse outcome , and peak burden of epilepsy is independently related to diagnosis of adverse outcome

    According to the time interval between the last EEG measurement (up to 72 hours of monitoring) and discharge: the time interval between the last measurement and discharge is <5 days


    The importance of this study is the discovery that the automatic measurement of the peak burden of epileptiform activities provides a convenient, consistent and quantifiable goal for future multicenter randomized trials to investigate whether inhibiting epileptiform activities can improve results


    Automatic measurement of peak epileptiform activity burden provides a convenient, consistent and quantifiable goal for future multicenter randomized trials.





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