A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Version 2 2024-06-05, 05:52
Version 1 2021-01-01, 00:00
journal contribution
posted on 2024-06-05, 05:52 authored by A Shoeibi, N Ghassemi, R Alizadehsani, M Rouhani, H Hosseini-Nejad, Abbas KhosraviAbbas Khosravi, M Panahiazar, S NahavandiA comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
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Location
Oxford, Eng.Language
EnglishPublication classification
C1 Refereed article in a scholarly journalCopyright notice
2020, Crown CopyrightJournal
Expert Systems with ApplicationsVolume
163Article number
ARTN 113788Pagination
113788 - 113788ISSN
0957-4174eISSN
1873-6793Publisher
PERGAMON-ELSEVIER SCIENCE LTDUsage metrics
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No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Artificial IntelligenceEngineering, Electrical & ElectronicOperations Research & Management ScienceComputer ScienceEngineeringEpileptic seizuresElectroencephalography (EEG)Convolutional autoencoderFeature extractionComputational complexityFEATURE-EXTRACTIONWAVELET TRANSFORMAUTOMATED DIAGNOSISNEURAL-NETWORKSCLASSIFICATIONENTROPYIDENTIFICATIONREPRESENTATIONDIMENSIONPATTERNGTP ResearchInstitute for Intelligent Systems Research and Innovation
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