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Author (up) Holguin-Garcia, S.A.; Guevara-Navarro, E.; Daza-Chica, A.E.; Patiño-Claro, M.A.; Arteaga-Arteaga, H.B.; Ruz, G.A.; Tabares-Soto, R.; Bravo-Ortiz, M.A. doi  openurl
  Title A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure Type
  Year 2024 Publication BMC Medical Informatics and Decision Making Abbreviated Journal BMC Med. Inform. Decis. Mak.  
  Volume 24 Issue 1 Pages 60  
  Keywords Capsule-Net; Electroencephalograms; Epilepsy; Machine learning; Transformer Encoder  
  Abstract IntroductionEpilepsy is a disease characterized by an excessive discharge in neurons generally provoked without any external stimulus, known as convulsions. About 2 million people are diagnosed each year in the world. This process is carried out by a neurological doctor using an electroencephalogram (EEG), which is lengthy.MethodTo optimize these processes and make them more efficient, we have resorted to innovative artificial intelligence methods essential in classifying EEG signals. For this, comparing traditional models, such as machine learning or deep learning, with cutting-edge models, in this case, using Capsule-Net architectures and Transformer Encoder, has a crucial role in finding the most accurate model and helping the doctor to have a faster diagnosis.ResultIn this paper, a comparison was made between different models for binary and multiclass classification of the epileptic seizure detection database, achieving a binary accuracy of 99.92% with the Capsule-Net model and a multiclass accuracy with the Transformer Encoder model of 87.30%.Conclusion Artificial intelligence is essential in diagnosing pathology. The comparison between models is helpful as it helps to discard those that are not efficient. State-of-the-art models overshadow conventional models, but data processing also plays an essential role in evaluating the higher accuracy of the models.  
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  ISSN 1472-6947 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:001176512800001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1974  
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