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Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection by Truong, Nhan; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Faulks, Andrew; Kavehei, Omid is a scholarly article available to read on EtoBox.

What is Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection about?

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 iEEGchannelswhich 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 acces

Author

Truong, Nhan; Kuhlmann, Levin; Bonyadi, Mohammad Reza; Yang, Jiawei; Faulks, Andrew; Kavehei, Omid

Published

2017

Language

EN