[The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 39(6): 1233-1239, 2022 Dec 25.
Article
em Zh
| MEDLINE
| ID: mdl-36575093
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.
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Texto completo:
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Base de dados:
MEDLINE
Assunto principal:
Doença de Alzheimer
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
Limite:
Humans
Idioma:
Zh
Ano de publicação:
2022
Tipo de documento:
Article