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

• Published in: BMC medical informatics and decision making Vol. 24; no. 1; p. 60
• Main Authors: Holguin-Garcia, Sergio Alejandro, Guevara-Navarro, Ernesto, Daza-Chica, Alvaro Eduardo, Patiño-Claro, Maria Alejandra, Arteaga-Arteaga, Harold Brayan, Ruz, Gonzalo A, Tabares-Soto, Reinel, Bravo-Ortiz, Mario Alejandro
• Format: Journal Article
• Language: English
• Published: England BioMed Central 01.03.2024; BMC
• Subjects: Accuracy; Algorithms; Artificial intelligence; Capsule-Net; Classification; Coders; Comparative studies; Convulsions; Convulsions & seizures; Data processing; Deep learning; Disease; EEG; Electroencephalograms; Electroencephalography; Epilepsy; Fourier transforms; Learning algorithms; Machine learning; Medical diagnosis; Model accuracy; Mortality; Neural networks; Seizures; Signal classification; Support vector machines; Transformer Encoder; Transformers; Wavelet transforms; Capsule-Net; Transformer Encoder; Electroencephalograms; Epilepsy; Machine learning
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02460-z

Epilepsy 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. To 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. In 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%. 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.