Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

Permutation Entropy-Based Interpretability of Convolutional Neural Network Models for Interictal EEG Discrimination of Subjects with Epileptic Seizures vs. Psychogenic Non-Epileptic Seizures

The differential diagnosis of epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) may be difficult, due to the lack of distinctive clinical features. The interictal electroencephalographic (EEG) signal may also be normal in patients with ES. Innovative diagnostic tools that exploit...

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Bibliographic Details
Main Authors: Michele Lo Giudice, Giuseppe Varone, Cosimo Ieracitano, Nadia Mammone, Giovanbattista Gaspare Tripodi, Edoardo Ferlazzo, Sara Gasparini, Umberto Aguglia, Francesco Carlo Morabito
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:Entropy
Subjects:
EEG
PNES
epilepsy
machine learning
deep learning
convolutional neural network
Online Access:https://www.mdpi.com/1099-4300/24/1/102

Internet

https://www.mdpi.com/1099-4300/24/1/102

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