脑电图
疾病
神经生理学
计算机科学
深层神经网络
人工智能
人工神经网络
医学
机器学习
神经科学
心理学
病理
作者
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
标识
DOI:10.1016/j.eswa.2022.118511
摘要
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.
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