Multimodal Detection of Early Partial Seizure Onsets

Johns Hopkins University, Baltimore MD

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Abstract

DESCRIPTION (provided by applicant): There is renewed interest for closed-loop therapy (neurostimulation, cooling, and medication) as an alternative method of seizure treatment for patients who have failed several antiepileptic drugs and are not surgery candidates. Responsive closed-loop therapy is designed to intervene within seconds after seizure onset to terminate seizure activity or prevent evolution to a disabling seizure. The challenge to closed-loop therapy is the requirement of very early detection while still retaining specificity, so the therapy can be administered early enough and still remains safe. One limitation of designing more specific detection methods is that partial seizure onset features are not as clearly established as those of primary generalized seizures. Although onsets of partial seizures can be somewhat stereotypical for the same patient, overall partial seizure onsets are typically more subtle, more slowly evolving and above all, more heterogeneous. Taking into account this variability of onset patterns, the design of a more adaptive paradigm of detection may be achieved using a multimodal approach which will assess the changes of intracranial EEG properties at the onset of partial seizures including commonly observed changes in frequency content but also changes in complexity and entropy. The ictal changes can be then characterized by the stereotypical ictal components occurring for all seizures in the same patient and the variant ictal components that are variable from seizure to seizure. Combinations of pattern recognition based methods to assess the stereotypical components and novelty detection based methods to assess the variant components of a seizure can improve the reliability of the early detection of partial seizure onsets. Improved early seizure detection can benefit not only the application of closed loop therapy, but also could be used for a reliable early warning system for patients.