期刊
HEALTH AND TECHNOLOGY
卷 9, 期 2, 页码 135-142出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s12553-018-0265-z
关键词
Bonn EEG database; Discrete wavelet transform (DWT); Electroencephalogram (EEG); Epileptic seizure; Multicenter; Freiburg EEG database
资金
- programme of State Scholarships Foundation (IKY)- by the European Union (European Social Fund - ESF)
- Greek national funds through the action entitled Strengthening Human Resources Research Potential via Doctorate Research - 2nd Cycle of the National Strategic Reference Framework (NSRF) 2014 - 2020
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.
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