1. Early diagnosis of Alzheimer's disease and mild cognitive impairment based on electroencephalography: From the perspective of event related potentials and deep learning.
- Author
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Wang, Chu, Xu, Tao, Yu, Wen, Li, Ting, Han, Huan, Zhang, Min, and Tao, Ming
- Subjects
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MILD cognitive impairment , *ALZHEIMER'S disease , *DEEP learning , *EARLY diagnosis , *ELECTROENCEPHALOGRAPHY - Abstract
Alzheimer's disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is generally prevalent in elderly people with significant disability and mortality. There is no effective treatment for AD currently, but the early diagnosis might be beneficial for delaying the disease progression. Apart from invasive laboratory tests and expensive neuroimaging examination, the electroencephalography (EEG) and event related potentials (ERPs) have emerged as promising approaches for the early detection of AD as well as mild cognitive impairment (MCI), due to its affordability, noninvasively, and superior temporal resolution. In addition, the recent advent of deep learning architectures further improves the accuracy of AD and MCI diagnosis. This article reviewed the application of EEG signal for the early diagnosis of AD and MCI, especially focusing on ERPs and deep learning. Furthermore, recommendation for further research to recruit the combination of ERP components and deep leaning models in diagnosing AD and MCI was proposed and highlighted. • Typical features of EEG and ERPs in AD and MCI have been repeatedly demonstrated, but the results were inconsistent. • The deep learning architectures greatly improve the accuracy of AD and MCI diagnosis through extracting features from EEG. • The combination of deep leaning and ERP components in the early diagnosis of AD has been highly recommended. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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