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Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
journal contribution
posted on 2021-01-01, 00:00 authored by A Malekzadeh, A Zare, M Yaghoobi, H R Kobravi, Roohallah AlizadehsaniRoohallah AlizadehsaniEpilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.
History
Journal
SensorsVolume
21Issue
22Article number
ARTN 7710Pagination
1 - 28Publisher
MDPILocation
Basel, SwitzlerlandPublisher DOI
ISSN
1424-8220eISSN
1424-8220Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
Keywords
ChemistryChemistry, AnalyticalCLASSIFICATIONCNN-RNNCOMPLEXITY ANALYSISCONVOLUTIONAL NEURAL-NETWORKDETRENDED FLUCTUATION ANALYSISdiagnosisEEGEngineeringEngineering, Electrical & Electronicepileptic seizuresFACTOR WAVELET TRANSFORMFUZZY ENTROPYIDENTIFICATIONInstruments & InstrumentationLOCALIZATIONnonlinear featuresPhysical SciencesScience & TechnologyTechnologyTQWTWEIGHTED VISIBILITY GRAPHCNN–RNNDistributed ComputingEcology
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