Supervised learning in automatic channel selection for epileptic seizure detection

Truong, N, Kuhlmann, L, Bonyadi, M, Yang, J, Faulks, A and Kavehei, O 2017, 'Supervised learning in automatic channel selection for epileptic seizure detection', Expert Systems with Applications, vol. 86, pp. 199-207.

Document type: Journal Article
Collection: Journal Articles

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Title Supervised learning in automatic channel selection for epileptic seizure detection
Author(s) Truong, N
Kuhlmann, L
Bonyadi, M
Yang, J
Faulks, A
Kavehei, O
Year 2017
Journal name Expert Systems with Applications
Volume number 86
Start page 199
End page 207
Total pages 9
Publisher Elsevier
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%.
Subject Medical Devices
Neurocognitive Patterns and Neural Networks
Knowledge Representation and Machine Learning
Keyword(s) Automatic channel selection
Random Forest
Seizure detection
DOI - identifier 10.1016/j.eswa.2017.05.055
Copyright notice © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
ISSN 0957-4174
Additional Notes Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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