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Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Int Burch University, Bosnia and Herceg.
Effat University, Saudi Arabia.
2018 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 39, 94-102 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2018. Vol. 39, 94-102 p.
Keyword [en]
Electroencephalography (EEG); Epilepsy; Seizure detection and prediction; Multiscale PCA (MSPCA); Discrete wavelet transform (DWT); Empirical mode decomposition (EMD); Wavelet packet decomposition (WPD)
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-142130DOI: 10.1016/j.bspc.2017.07.022ISI: 000412607900009OAI: oai:DiVA.org:liu-142130DiVA: diva2:1151726
Available from: 2017-10-24 Created: 2017-10-24 Last updated: 2017-10-24

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • Other style
More styles
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  • de-DE
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More languages
Output format
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