The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1233-1239, 2022.
Artículo
en Chino
| WPRIM
| ID: wpr-970662
ABSTRACT
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer's diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer's disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer's disease.
Texto completo:
Disponible
Índice:
WPRIM (Pacífico Occidental)
Asunto principal:
Encéfalo
/
Redes Neurales de la Computación
/
Electroencefalografía
/
Enfermedad de Alzheimer
/
Aprendizaje Automático
Límite:
Humanos
Idioma:
Chino
Revista:
Journal of Biomedical Engineering
Año:
2022
Tipo del documento:
Artículo
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