Predicting progression from amnestic mild cognitive impairment to Alzheimer's disease using longitudinal EEG data: a 12-month cohort study
By capturing trend information associated with disease progression, longitudinal EEG features contributed to enhancing prediction performance in machine learning models.
Authors
- Pan Zheng
- Shuo Yang (ORCID: https://orcid.org/0000-0001-6406-081X)
- Jin Zhang (ORCID: https://orcid.org/0000-0002-8397-5078)
- Chonglong Ding (ORCID: https://orcid.org/0009-0002-3799-0135)
- Yingfeng Ge (ORCID: https://orcid.org/0009-0007-9105-669X)
- Yingying Fang
- Jianan Yin (ORCID: https://orcid.org/0009-0004-9043-4415)
- Yupeng Zheng (ORCID: https://orcid.org/0000-0002-2165-7859)
- Yi Fei
- Xilin Lu
- Nanxiang Zhang
- Xiaohao Zhang
Institutions
- Sun Yat-sen University (CN)
- First People's Hospital of Yuhang District (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2026-01-20
- DOI
- https://doi.org/10.3389/fnagi.2025.1719981
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00