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.
脑电信号是脑神经电生理活动的总体反映，具有安全、高效、实时、动态等优点。随着机器学习相关研究的开展与推进，基于神经网络的阿尔茨海默病自动诊断正成为脑电分析研究热点。本文从前馈神经网络入手，比较分析了循环神经网络、卷积神经网络和深度信念网络等神经网络模型的结构特性及其在阿尔茨海默病诊断中的性能表现，并探讨了该研究在未来可能面临的挑战和研究趋势，期望为神经网络在阿尔茨海默病脑电诊断的临床应用提供有价值的参考。.
Keywords: Alzheimer’s disease; Deep learning; Electroencephalogram diagnosis; Neural network.