Individualized diagnosis of preclinical Alzheimer’s Disease using deep neural networks

• Published in: Expert systems with applications 2022-12, Vol.210, p.118511, Article 118511
• Main Authors: Park, Jinhee, Jang, Sehyeon, Gwak, Jeonghwan, Kim, Byeong C., Lee, Jang Jae, Choi, Kyu Yeong, Lee, Kun Ho, Jun, Sung Chan, Jang, Gil-Jin, Ahn, Sangtae
• Format: Article
• Language: English
• Subjects: Deep neural networks; Electroencephalography; Preclinical Alzheimer’s Disease
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.118511

The early diagnosis of Alzheimer’s Disease (AD) plays a central role in the treatment of AD. Particularly, identifying the preclinical AD (pAD) stage could be crucial for timely treatment in the elderly. However, screening participants with pAD requires a series of psychological and neurological examinations. Thus, an efficient diagnostic tool is needed. Here, we recruited 91 elderly participants and collected 1 min of resting-state electroencephalography data to classify participants as normal aging or diagnosed with pAD. We used deep neural networks (Deep ConvNet, EEGNet, EEG-TCNet, and cascade CRNN) in the within- and cross-subject paradigms for classification and found individual variations of classification accuracy in the cross-subject paradigm. Further, we proposed an individualized diagnostic strategy to identify neurophysiological similarities across participants and the proposed approach considering individual characteristics improved the diagnostic performance by approximately 20%. Our findings suggest that considering individual characteristics would be a breakthrough in diagnosing AD using deep neural networks.