Alzheimer's Disease Classification With a Cascade Neural Network

You, Zeng1,2; Zeng, Runhao2; Lan, Xiaoyong1; Ren, Huixia1,3; You, Zhiyang1,2; Shi, Xue1; Zhao, Shipeng1,2; Guo, Yi1; Jiang, Xin4; Hu, Xiping2

2020-11-03

10.3389/fpubh.2020.584387

Frontiers in Public Health   影响因子和分区

2296-2565

2296-2565

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 AD eventually. We collect gait and EEG data from 35 cognitively health controls, 35 MCI, and 17 AD patients to evaluate our proposed method. Experimental results show that our method significantly outperforms other AD diagnosis methods (91.07 vs. 68.18%) in the three-way AD classification task (HC, MCI, and AD). Moreover, we empirically found that the lower body and right upper limb are more important for the early diagnosis of AD than other body parts. We believe this interesting finding can be helpful for clinical researches.

Alzheimer&apos s disease deep learning automatic diagnosis gait EEG

SCI ; SSCI

英语

第一 ; 通讯

Science and Technology Planning Project of Shenzhen Municipality[JCYJ20170818111012390] ; Sanming Project of Medicine in Shenzhen[SYJY201905][SYJY201906] ; Shenzhen Health Committee Project[SZXJ2017034]

Public, Environmental & Occupational Health

Public, Environmental & Occupational Health

WOS:000589682400001

FRONTIERS MEDIA SA

Web of Science

1.Jinan Univ, Dept Neurol, Shenzhen Peoples Hosp, Affiliated Hosp 1,Southern Univ Sci & Technol,Sec, Shenzhen, Peoples R China
2.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen, Peoples R China
3.Jinan Univ, Affiliated Hosp 1, Guangzhou, Peoples R China
4.Jinan Univ, Dept Geriatr, Shenzhen Peoples Hosp, Affiliated Hosp 1,Southern Univ Sci & Technol,Sec, Shenzhen, Peoples R China

You, Zeng,Zeng, Runhao,Lan, Xiaoyong,et al. Alzheimer's Disease Classification With a Cascade Neural Network[J]. Frontiers in Public Health,2020,8.

You, Zeng.,Zeng, Runhao.,Lan, Xiaoyong.,Ren, Huixia.,You, Zhiyang.,...&Hu, Xiping.(2020).Alzheimer's Disease Classification With a Cascade Neural Network.Frontiers in Public Health,8.

You, Zeng,et al."Alzheimer's Disease Classification With a Cascade Neural Network".Frontiers in Public Health 8(2020).