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Int J Neural Syst. 2015 Aug;25(5):1550019. doi: 10.1142/S0129065715500197. Epub 2015 Mar 18.

Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure.

Author information

1
Department of Informatics Engineering, University of Coimbra, Portugal.
2
Department of Electrical and Computer Engineering, Noshirvani University of Technology, Iran.
3
Netoff Epilepsy Lab, Department of Biomedical Engineering, University of Minnesota, USA.
4
Department of Electrical and Computer Engineering, University of Minnesota, USA.

Abstract

A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram (iEEG) electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. The NPS measure is obtained from the spectral analysis of space-differential iEEG signals. Ratio between the NPS values obtained from two specific frequency bands is then investigated as a robust generalized measure, and reveals invaluable information about seizure initiation trends. A threshold-based classifier is subsequently applied on the proposed measure to generate alarms. The performance of the method was evaluated using cross-validation on a large clinical dataset, involving 183 seizure onsets in 1785 h of long-term continuous iEEG recordings of 11 patients. On average, the results show a high sensitivity of 86.9% (159 out of 183), a very low false detection rate of 1.4 per day, and a mean detection latency of 13.1 s from electrographic seizure onsets, while in average preceding clinical onsets by 6.3 s. These high performance results, specifically the short detection latency, coupled with the very low computational cost of the proposed method make it adequate for using in implantable closed-loop seizure suppression systems.

KEYWORDS:

Epilepsy; early seizure detection; neuronal potential similarity; power spectral density; responsive neurostimulation; synchronization

PMID:
25997912
DOI:
10.1142/S0129065715500197
[Indexed for MEDLINE]

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