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A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
journal contribution
posted on 2021-01-01, 00:00 authored by A Shoeibi, N Ghassemi, Roohallah Alizadehsani, M Rouhani, H Hosseini-Nejad, Abbas KhosraviAbbas Khosravi, M Panahiazar, Saeid NahavandiEpilepsy, a brain disease generally associated with seizures, has tremendous effects on people’s quality of life. Diagnosis of epileptic seizures is commonly performed on electroencephalography (EEG) signals, and by using computer-aided diagnosis systems (CADS), neurologists can diagnose epileptic seizure stages more accurately. In these systems, a mandatory stage is feature extraction, performed by handcrafting features or learning them, ordinarily by a deep neural net. While researches in this field commonly show the value of a group of limited features, yet an accurate comparison between different suggested features is essential. In this article, first, a comparison between the importance of 50 different handcrafted features for seizure detection is presented. Additionally, the computational complexity of features is investigated as well. Then the best features based on Fisher scores are picked to classify signals on a benchmark dataset for evaluation. Additionally, a convolutional autoencoder with five layers is applied to learn features in order to have a complete comparison among feature extraction approaches. Finally, a hybrid method is employed, which combines handcrafted features and encoding of autoencoder to reach high performance in seizure detection in EEG signals.
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Journal
Expert Systems with ApplicationsVolume
163Article number
113788Pagination
113788 - 113788Publisher
ElsevierLocation
Oxford, Eng.Publisher DOI
ISSN
0957-4174Language
engPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, Crown CopyrightUsage metrics
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No categories selectedKeywords
Epileptic seizuresElectroencephalography (EEG)Convolutional autoencoderFeature extractionComputational complexityScience & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringFEATURE-EXTRACTIONWAVELET TRANSFORMAUTOMATED DIAGNOSISNEURAL-NETWORKSCLASSIFICATIONENTROPYIDENTIFICATIONREPRESENTATIONDIMENSIONPATTERN
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