Alzheimer's Disease Classification With a Cascade Neural Network

作者：Zeng You, Runhao Zeng, Xiaoyong Lan, Huixia Ren, Zhiyang You, Xue Shi, Shipeng Zhao, Yi Guo, Xin Jiang, Xiping Hu · 发表于：Frontiers in Public Health · 年份：2020 · DOI：10.3389/fpubh.2020.584387 · 被引用次数：46 · 研究领域：EEG and Brain-Computer Interfaces、Gait Recognition and Analysis、Context-Aware Activity Recognition Systems

Classification of Alzheimer's Disease (AD) has been becoming a hot issue along with the rapidly increasing number of patients. This task remains tremendously challenging due to the limited data and the difficulties in detecting mild cognitive impairment (MCI). Existing methods use gait [or EEG (electroencephalogram)] data only to tackle this task. Although the gait data acquisition procedure is cheap and simple, the methods relying on gait data often fail to detect the slight difference between MCI and AD. The methods that use EEG data can detect the difference more precisely, but collecting EEG data from both HC (health controls) and patients is very time-consuming. More critically, these methods often convert EEG records into the frequency domain and thus inevitably lose the spatial and temporal information, which is essential to capture the connectivity and synchronization among different brain regions. This paper proposes a cascade neural network with two steps to achieve a faster and more accurate AD classification by exploiting gait and EEG data simultaneously. In the first step, we propose attention-based spatial temporal graph convolutional networks to extract the features from the skeleton sequences (i.e., gait) captured by Kinect (a commonly used sensor) to distinguish between HC and patients. In the second step, we propose spatial temporal convolutional networks to fully exploit the spatial and temporal information of EEG data and classify the patients into MCI or ...