Predicting states of epilepsy patients using deep learning models

• Autorzy: Sidaoui Boutkhil

Identyfikatory

DOI

10.35784/acs-2024-19

• Języki publikacji: EN

Abstrakty

EN

In this study, the authors present and scrutinize two deep learning models designed for predicting the states of epilepsy patients by utilizing extracted data from their brain's electrical activities recorded in electroencephalography (EEG) signals. The proposed models leverage deep learning networks, with the first being a recurrent neural network known as Long Short-Term Memory (LSTM), and the second a non-recurrent network in the form of a Deep Feedforward Network (DFN) architecture. To construct and execute the DFN and LSTM architectures, the authors rely on 22 characteristics extracted from diverse EEG signals, forming a comprehensive dataset from five patients. The primary goal is to forecast impending epilepsy seizures and categorize three distinct states of brain activity in epilepsy patients. The models put forward yield promising results, particularly in terms of classification rates, across various preceding seizure timeframes ranging from 5 to 50 minutes.

Słowa kluczowe

EN

epilepsy seizure; EEG; prediction; Deep Learning; LSTM

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2024
• Tom: Vol. 20, no. 2
• Strony: 109--125
• Opis fizyczny: Bibliogr. 22 poz., fig., tab.

Twórcy

autor

Sidaoui Boutkhil

• University Center SALHI Ahmed, Computer Science Department, Naâma, Algeria

• https://orcid.org/0000-0001-7276-2897

Bibliografia

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