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

Restricted access


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.


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.


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.


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.

ABBREVIATIONS DBS = deep brain stimulation; EEG = electroencephalography; LFP = local field potential; ROC = receiver operating characteristic; SI = similarity index.

Article Information

INCLUDE WHEN CITING Published online October 7, 2016; DOI: 10.3171/2016.7.JNS161282.

Drs. So and Krishna contributed equally to this work.

Correspondence Vibhor Krishna, Department of Neurosurgery and Department of Neuroscience, The Ohio State University, 480 Medical Center Dr., #1019, Columbus, OH 43220. email:

© AANS, except where prohibited by US copyright law.



  • View in gallery

    Method used in signal processing and analysis. A: Block diagram showing steps for signal processing. B: Example test window showing the steps involved in the calculation of the SI. Two examples are shown for the projection of the time series onto the first 2 transform axes, resulting in one high SI value and another moderate SI value. Figure is available in color online only.

  • View in gallery

    Patient A. Example recording segment and analysis. A: Raw recordings from one bipolar thalamic DBS channel A. Seizure onset is indicated by the red vertical line. B: Spectrogram for recording in panel A. C: SI calculated for recording in panel. A. A decrease in SI is seen before the onset of seizure. D: SI for a scalp EEG channel. No apparent decrease in SI is observed before seizure onset, but SI decreases sharply after seizure onset. Figure is available in color online only.

  • View in gallery

    Examples of prediction, detection, and false alarm. A–F: Examples of true prediction (green arrow), true detection (blue arrow), and false alarm (red arrow) for 3 patients using thalamic DBS recordings (A, C, and E) and scalp EEG recordings (B, D, and F). The red vertical line represents seizure onset time; the green horizontal line represents threshold for alarm. G and H: The mean SI for DBS and EEG recordings. Figure is available in color online only.

  • View in gallery

    ROC curves for detection (A) and prediction (B) of seizures. ROC curves for DBS are above those for EEG, indicating better algorithm performance with thalamic DBS recordings. Figure is available in color online only.



Cited By



All Time Past Year Past 30 Days
Abstract Views 31 31 25
Full Text Views 243 243 3
PDF Downloads 132 132 1
EPUB Downloads 0 0 0


Google Scholar