A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.

"availability of data and materials the datasets and code used during the current study are available at: https://github com/bioaiteam/a-comparative-study-of-cnn-capsule-net-cnn-transformer-encoder-and-traditional-machine-learning-al ."

"availability of data and materials the datasets and code used during the current study are available at: https://github com/bioaiteam/a-comparative-study-of-cnn-capsule-net-cnn-transformer-encoder-and-traditional-machine-learning-al ."

"Declarations Ethics approval and consent to participateNot applicable. Consent for publicationNot applicable. Competing interestsThe authors declare no competing interests. Competing interests The authors declare no competing interests."

"Funding This work was funded by Universidad Autonoma de Manizales as part of the project “Clasificación de los estadios del Alzheimer utilizando Imágenes de Resonancia Magnética Nuclear y datos clínicos a partir de técnicas de Deep Learning” with code 873-139, and also by the projects “CH-T1246: Oportunidades de Mercado para las Empresas de Tecnología-Compras Públicas de Algoritmos Responsables, Éticos y Transparentes”, ANID PIA/BASAL FB0002, and ANID/PIA/ANILLOS ACT210096."