Elsevier

Expert Systems with Applications

Volume 86, 15 November 2017, Pages 199-207
Expert Systems with Applications

Supervised learning in automatic channel selection for epileptic seizure detection

https://doi.org/10.1016/j.eswa.2017.05.055Get rights and content
Under a Creative Commons license
open access

Highlights

  • An automatic channel selection for seizure detection is proposed.

  • Computational efficiency is improved by 49.4%, while maintaining accuracy close to 96.5%.

  • Mean detection delay is improved by 400 ms to 2.77s without degrading specificity.

  • Seizure onsets are detected at 91.95% sensitivity and 94.05% specificity.

Abstract

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%.

Keywords

Seizure detection
iEEG
Random Forest
Automatic channel selection

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