Journal
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Volume 39, Issue -, Pages 94-102Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2017.07.022
Keywords
Electroencephalography (EEG); Epilepsy; Seizure detection and prediction; Multiscale PCA (MSPCA); Discrete wavelet transform (DWT); Empirical mode decomposition (EMD); Wavelet packet decomposition (WPD)
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This study proposes a new model which is fully specified for automated seizure onset detection and seizure onset prediction based on electroencephalography (EEG) measurements. We processed two archetypal EEG databases, Freiburg (intracranial EEG) and CHB-MIT (scalp EEG), to find if our model could outperform the state-of-the art models. Four key components define our model: (1) multiscale principal component analysis for EEG de-noising, (2) EEG signal decomposition using either empirical mode decomposition, discrete wavelet transform or wavelet packet decomposition, (3) statistical measures to extract relevant features, (4) machine learning algorithms. Our model achieved overall accuracy of 100% in ictal vs. inter-ictal EEG for both databases. In seizure onset prediction, it could discriminate between inter-ictal, pre-ictal, and ictal EEG with the accuracy of 99.77%, and between inter-ictal and pre-ictal EEG states with the accuracy of 99.70%. The proposed model is general and should prove applicable to other classification tasks including detection and prediction regarding bio-signals such as EMG and ECG. (C) 2017 Elsevier Ltd. All rights reserved.
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