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J Neurosurg. 2017 Jun;126(6):2036-2044. doi: 10.3171/2016.7.JNS161282. Epub 2016 Oct 7.

Prediction and detection of seizures from simultaneous thalamic and scalp electroencephalography recordings.

Author information

1
Institute for Infocomm Research.
2
Department of Neurosurgery, Center for Neuromodulation, and.
3
Department of Neuroscience, The Ohio State University, Columbus, Ohio; and.
4
National Neuroscience Institute, and.
5
Divisions of 5 Neurosurgery and.
6
Neurology, Krembil Neuroscience Centre, University of Toronto, Ontario, Canada.
7
Nanyang Technological University, Singapore.

Abstract

OBJECTIVE The authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy. METHODS Using data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear measure for seizure identification, with patient-specific channel and threshold selection. Receiver operating characteristic (ROC) curves were calculated using data from all patients and channels to compare the performance between DBS and EEG recordings. RESULTS Thalamic DBS recordings were associated with a mean prediction rate of 84%, detection rate of 97%, and false-alarm rate of 0.79/hr. In comparison, scalp EEG recordings were associated with a mean prediction rate of 71%, detection rate of 100%, and false-alarm rate of 1.01/hr. From the ROC curves, when considering all channels, DBS outperformed EEG for both detection and prediction of seizures. CONCLUSIONS This is the first study to compare automated seizure detection and prediction from simultaneous thalamic and scalp EEG recordings. The authors have demonstrated that signals recorded from DBS leads are more robust than EEG recordings and can be used to predict and detect seizures. These results indicate feasibility for future designs of closed-loop anterior nucleus DBS systems for the treatment of epilepsy.

KEYWORDS:

DBS = deep brain stimulation; EEG; EEG = electroencephalography; LFP = local field potential; ROC = receiver operating characteristic; SI = similarity index; closed-loop feedback; deep brain stimulation; detection; epilepsy; prediction; thalamic DBS

PMID:
27715438
DOI:
10.3171/2016.7.JNS161282

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