Cargando…
A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction
Epileptic seizure detection has undergone progressive advancements since its conception in the 1970s. From proof-of-concept experiments in the latter part of that decade, it has now become a vibrant area of clinical and laboratory research. In an effort to bring this technology closer to practical a...
Autores principales: | Palanisamy, Preethi, Urooj, Shabana, Arunachalam, Rajesh, Lay-Ekuakille, Aime |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10650532/ https://www.ncbi.nlm.nih.gov/pubmed/37958278 http://dx.doi.org/10.3390/diagnostics13213382 |
Ejemplares similares
-
A Deep Learning-Based Framework for Retinal Disease Classification
por: Choudhary, Amit, et al.
Publicado: (2023) -
Epileptic Seizure Detection Based on EEG Signals and CNN
por: Zhou, Mengni, et al.
Publicado: (2018) -
An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy
por: Chen, Wenna, et al.
Publicado: (2023) -
Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data
por: Hassan, Fatima, et al.
Publicado: (2022) -
An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model
Publicado: (2023)