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

Jinhee Park, Sehyeon Jang, Jeonghwan Gwak, Byeong C. Kim, Jang Jae Lee, Kyu Yeong Choi, Kun Ho Lee, Sung Chan Jun, Gil-Jin Jang, Sangtae Ahn

Abstract: 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.