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J Neural Eng. 2016 Jun;13(3):036011. doi: 10.1088/1741-2560/13/3/036011. Epub 2016 Apr 21.

A novel seizure detection algorithm informed by hidden Markov model event states.

Author information

1
Department of Bioengineering, University of Pennsylvania, USA. Center for Neuroengineering and Therapeutics, University of Pennsylvania, USA.

Abstract

OBJECTIVE:

Recently the FDA approved the first responsive, closed-loop intracranial device to treat epilepsy. Because these devices must respond within seconds of seizure onset and not miss events, they are tuned to have high sensitivity, leading to frequent false positive stimulations and decreased battery life. In this work, we propose a more robust seizure detection model.

APPROACH:

We use a Bayesian nonparametric Markov switching process to parse intracranial EEG (iEEG) data into distinct dynamic event states. Each event state is then modeled as a multidimensional Gaussian distribution to allow for predictive state assignment. By detecting event states highly specific for seizure onset zones, the method can identify precise regions of iEEG data associated with the transition to seizure activity, reducing false positive detections associated with interictal bursts. The seizure detection algorithm was translated to a real-time application and validated in a small pilot study using 391 days of continuous iEEG data from two dogs with naturally occurring, multifocal epilepsy. A feature-based seizure detector modeled after the NeuroPace RNS System was developed as a control.

MAIN RESULTS:

Our novel seizure detection method demonstrated an improvement in false negative rate (0/55 seizures missed versus 2/55 seizures missed) as well as a significantly reduced false positive rate (0.0012 h versus 0.058 h(-1)). All seizures were detected an average of 12.1 ± 6.9 s before the onset of unequivocal epileptic activity (unequivocal epileptic onset (UEO)).

SIGNIFICANCE:

This algorithm represents a computationally inexpensive, individualized, real-time detection method suitable for implantable antiepileptic devices that may considerably reduce false positive rate relative to current industry standards.

PMID:
27098152
PMCID:
PMC4888894
DOI:
10.1088/1741-2560/13/3/036011
[Indexed for MEDLINE]
Free PMC Article

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