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Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy
<span title="">2014</span>
<i title="Elsevier BV">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sprqq3w7qnbabl7xhdmtiibsfq" style="color: black;">Epilepsy & Behavior</a>
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Artificial neural network Automated seizure detection Closed-loop methods ECG-based seizure detection EEG-based seizure detection Fourier Higher-order spectra Markov modeling Support vector machine Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP
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<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.yebeh.2014.06.023">doi:10.1016/j.yebeh.2014.06.023</a>
<a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25174001">pmid:25174001</a>
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... evention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.
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