The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease.
10.7507/1001-5515.202201001
- Author:
Yi LIU
1
;
Zhenyang LI
1
;
Zhiwei WEI
1
;
Yonghong XU
1
;
Ping XIE
1
;
Yulin WANG
2
;
Qinshuang LIU
2
;
Xin LI
1
Author Information
1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P. R. China.
2. Qinhuangdao First Hospital, Qinhuangdao, Hebei 066004, P. R. China.
- Publication Type:Journal Article
- Keywords:
Alzheimer’s disease;
Deep learning;
Electroencephalogram diagnosis;
Neural network
- MeSH:
Humans;
Alzheimer Disease/diagnosis*;
Neural Networks, Computer;
Machine Learning;
Brain;
Electroencephalography
- From:
Journal of Biomedical Engineering
2022;39(6):1233-1239
- CountryChina
- Language:Chinese
-
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.
