alizadehsani-automaticdiagnosis-2021.pdf (12.01 MB)
Automatic diagnosis of epileptic seizures in EEG signals using fractal dimension features and convolutional autoencoder method
journal contribution
posted on 2021-01-01, 00:00 authored by A Malekzadeh, A Zare, M Yaghoobi, Roohallah AlizadehsaniRoohallah AlizadehsaniThis paper proposes a new method for epileptic seizure detection in electroencephalography (EEG) signals using nonlinear features based on fractal dimension (FD) and a deep learning (DL) model. Firstly, Bonn and Freiburg datasets were used to perform experiments. The Bonn dataset consists of binary and multi-class classification problems, and the Freiburg dataset consists of two-class EEG classification problems. In the preprocessing step, all datasets were prepossessed using a Butterworth band pass filter with 0.5–60 Hz cut-off frequency. Then, the EEG signals of the datasets were segmented into different time windows. In this section, dual-tree complex wavelet transform (DT-CWT) was used to decompose the EEG signals into the different sub-bands. In the following section, in order to feature extraction, various FD techniques were used, including Higuchi (HFD), Katz (KFD), Petrosian (PFD), Hurst exponent (HE), detrended fluctuation analysis (DFA), Sevcik, box counting (BC), multiresolution box-counting (MBC), Margaos-Sun (MSFD), multifractal DFA (MF-DFA), and recurrence quantification analysis (RQA). In the next step, the minimum redundancy maximum relevance (mRMR) technique was used for feature selection. Finally, the k-nearest neighbors (KNN), support vector machine (SVM), and convolutional autoencoder (CNN-AE) were used for the classification step. In the classification step, the K-fold cross-validation with k = 10 was employed to demonstrate the effectiveness of the classifier methods. The experiment results show that the proposed CNN-AE method achieved an accuracy of 99.736% and 99.176% for the Bonn and Freiburg datasets, respectively.
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Journal
Big Data and Cognitive ComputingVolume
5Issue
4Article number
ARTN 78Pagination
1 - 31Publisher
MDPI / MDPI AG (Multidisciplinary Digital Publishing Institute)Location
Basel, SwitzerlandPublisher DOI
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eISSN
2504-2289Language
EnglishPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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CLASSIFICATIONCNN-AEComputer ScienceComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsdiagnosisEEGepileptic seizuresfractal dimensionsIDENTIFICATIONMACHINEmRMRNETWORKNONLINEAR FEATURESPERFORMANCEPREDICTIONScience & TechnologyTechnologyWAVELET TRANSFORM
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