ArticleFrontiers in aging neuroscience2022
Sleep EEG-Based Approach to Detect Mild Cognitive Impairment.
Article in Frontiers in aging neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- Wearable-Compatible Detection of Mild Cognitive Impairment Using Novel Features Based on Sleep Stage Dynamics.Brain sciences · 2026Article
- A deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG.Brain informatics · 2026Article
- Wearable EEG devices in the detection of mild cognitive impairment: a systematic review.NPJ digital medicine · 2026Article
- Integrating memory-guided saccades and EEG for MCI screening: a multimodal LASSO modeling approach.Frontiers in aging neuroscience · 2026Article
- Sleep electroencephalography-derived brain health and long-term cognitive decline in community-dwelling adults.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.Cognitive neurodynamics · 2024Article
- Consensus on rapid screening for prodromal Alzheimer's disease in China.General psychiatry · 2024Review
- Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach.Frontiers in aging neuroscience · 2024Article
- Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study.Sleep advances : a journal of the Sleep Research Society · 2024Article
- Screening for Mild Cognitive Impairment with Speech Interaction Based on Virtual Reality and Wearable Devices.Brain sciences · 2023Article
- Exploring the Link Between Brain Waves and Sleep Patterns with Deep Learning Manifold Alignment.The 4th Joint International Conference on Deep Learning, Big Data and Blockchain (DBB 2023). Joint International Conference on Deep Learning, Big Data and Blockchain (4th : 2023 : Marrakech, Morocco ; Online) · 2023Article
- [The current applicating state of neural network-based electroencephalogram diagnosis of Alzheimer's disease].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Mild Cognitive Impairment (MCI) is an early stage of dementia, which may lead to Alzheimer's disease (AD) in older adults. Therefore, early detection of MCI and implementation of treatment and intervention can effectively slow down or even inhibit the progression of the disease, thus minimizing the risk of AD. Currently, we know that published work relies on an analysis of awake EEG recordings. However, recent studies have suggested that changes in the structure of sleep may lead to cognitive decline. In this work, we propose a sleep EEG-based method for MCI detection, extracting specific features of sleep to characterize neuroregulatory deficit emergent with MCI. This study analyzed the EEGs of 40 subjects (20 MCI, 20 HC) with the developed algorithm. We extracted sleep slow waves and spindles features, combined with spectral and complexity features from sleep EEG, and used the SVM classifier and GRU network to identify MCI. In addition, the classification results of different feature sets (including with sleep features from sleep EEG and without sleep features from awake EEG) and different classification methods were evaluated. Finally, the MCI classification accuracy of the GRU network based on features extracted from sleep EEG was the highest, reaching 93.46%. Experimental results show that compared with the awake EEG, sleep EEG can provide more useful information to distinguish between MCI and HC. This method can not only improve the classification performance but also facilitate the early intervention of AD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.