A robust methodology for classification of epileptic seizures in EEG signals

Drug inefficiency in patients with refractory seizures renders epilepsy a life-threatening and challenging brain disorder and stresses the need for accurate seizure detection and prediction methods and more personalized closed-loop treatment systems. In this paper, a multicenter methodology for auto...

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Bibliographic Details
Published in:Health and technology 2019-03, Vol.9 (2), p.135-142
Main Authors: Tzimourta, Katerina D., Tzallas, Alexandros T., Giannakeas, Nikolaos, Astrakas, Loukas G., Tsalikakis, Dimitrios G., Angelidis, Pantelis, Tsipouras, Markos G.
Format: Article
Language:English
Subjects:
Accuracy
Automation
Bandwidths
Biological and Medical Physics
Biomedical Engineering and Bioengineering
Biomedicine
Biophysics
Brain research
Classification
Closed loops
Computational Biology/Bioinformatics
Convulsions & seizures
Discrete Wavelet Transform
Electroencephalography
Engineering
Epilepsy
Mathematical functions
Medicine/Public Health
Methodology
Methods
Neurological disorders
Original Paper
Patients
Principal components analysis
R & D/Technology Policy
Robustness
Seizures
Support vector machines
Wavelet transforms
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Summary:Drug inefficiency in patients with refractory seizures renders epilepsy a life-threatening and challenging brain disorder and stresses the need for accurate seizure detection and prediction methods and more personalized closed-loop treatment systems. In this paper, a multicenter methodology for automated seizure detection based on Discrete Wavelet Transform (DWT) is presented. A decomposition of 5 levels is applied in each EEG segment and five features are extracted from the wavelet coefficients. The extracted feature vector is used to train a Random Forest classifier and discriminate between ictal and interictal data. EEG recordings from the database of University of Bonn and the database of the University Hospital of Freiburg were employed, in an attempt to test the efficiency and robustness of the method. Classification results in both databases are significant, reaching accuracy above 95% and confirming the robustness of the methodology. Sensitivity and False Positive Rate for the Freiburg database reached 99.74% and 0.21/h respectively.
ISSN:2190-7188
2190-7196
DOI:10.1007/s12553-018-0265-z