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      Seizure detection algorithm based on improved functional brain network structure feature extraction

      , , , , ,
      Biomedical Signal Processing and Control
      Elsevier BV

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          PhysioBank, PhysioToolkit, and PhysioNet

          Circulation, 101(23)
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            Epilepsy in adults

            Epilepsy is one of the most common serious brain conditions, affecting over 70 million people worldwide. Its incidence has a bimodal distribution with the highest risk in infants and older age groups. Progress in genomic technology is exposing the complex genetic architecture of the common types of epilepsy, and is driving a paradigm shift. Epilepsy is a symptom complex with multiple risk factors and a strong genetic predisposition rather than a condition with a single expression and cause. These advances have resulted in the new classification of epileptic seizures and epilepsies. A detailed clinical history and a reliable eyewitness account of a seizure are the cornerstones of the diagnosis. Ancillary investigations can help to determine cause and prognosis. Advances in brain imaging are helping to identify the structural and functional causes and consequences of the epilepsies. Comorbidities are increasingly recognised as important aetiological and prognostic markers. Antiseizure medication might suppress seizures in up to two-thirds of all individuals but do not alter long-term prognosis. Epilepsy surgery is the most effective way to achieve long-term seizure freedom in selected individuals with drug-resistant focal epilepsy, but it is probably not used enough. With improved understanding of the gradual development of epilepsy, epigenetic determinants, and pharmacogenomics comes the hope for better, disease-modifying, or even curative, pharmacological and non-pharmacological treatment strategies. Other developments are clinical implementation of seizure detection devices and new neuromodulation techniques, including responsive neural stimulation.
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              Is Open Access

              Deep learning for electroencephalogram (EEG) classification tasks: a review

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                Author and article information

                Contributors
                Journal
                Biomedical Signal Processing and Control
                Biomedical Signal Processing and Control
                Elsevier BV
                17468094
                January 2023
                January 2023
                : 79
                : 104053
                Article
                10.1016/j.bspc.2022.104053
                34bfa903-7df2-4c0d-a50a-29ede08a07f0
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                https://doi.org/10.15223/policy-017

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-012

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-004

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