ArticleSensors (Basel, Switzerland)2021
A Deep Learning Strategy for Automatic Sleep Staging Based on Two-Channel EEG Headband Data.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Psychotic relapse prediction via biomarker monitoring: a systematic review.Frontiers in psychiatry · 2024Pooled it
- Evaluating the performance of wearable EEG sleep monitoring devices: a meta-analysis approach.npj biomedical innovations · 2025Article
- Automated Sleep Stage Classification Using PSO-Optimized LSTM on CAP EEG Sequences.Brain sciences · 2025Article
- Article
- An Effective and Interpretable Sleep Stage Classification Approach Using Multi-Domain Electroencephalogram and Electrooculogram Features.Bioengineering (Basel, Switzerland) · 2025Article
- [Automatic sleep staging model based on single channel electroencephalogram signal].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Does EGFR Signaling Mediate Orexin System Activity in Sleep Initiation?International journal of molecular sciences · 2023Review
- A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.Biosensors · 2022Article
- Home EEG sleep assessment shows reduced slow-wave sleep in mild-moderate Alzheimer's disease.Alzheimer's & dementia (New York, N. Y.) · 2022Article
- Article
- Sleep Monitoring of Children With Nocturnal Enuresis: A Narrative Review.Frontiers in pediatrics · 2021Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep disturbances are common in Alzheimer's disease and other neurodegenerative disorders, and together represent a potential therapeutic target for disease modification. A major barrier for studying sleep in patients with dementia is the requirement for overnight polysomnography (PSG) to achieve formal sleep staging. This is not only costly, but also spending a night in a hospital setting is not always advisable in this patient group. As an alternative to PSG, portable electroencephalography (EEG) headbands (HB) have been developed, which reduce cost, increase patient comfort, and allow sleep recordings in a person's home environment. However, naïve applications of current automated sleep staging systems tend to perform inadequately with HB data, due to their relatively lower quality. Here we present a deep learning (DL) model for automated sleep staging of HB EEG data to overcome these critical limitations. The solution includes a simple band-pass filtering, a data augmentation step, and a model using convolutional (CNN) and long short-term memory (LSTM) layers. With this model, we have achieved 74% (±10%) validation accuracy on low-quality two-channel EEG headband data and 77% (±10%) on gold-standard PSG. Our results suggest that DL approaches achieve robust sleep staging of both portable and in-hospital EEG recordings, and may allow for more widespread use of ambulatory sleep assessments across clinical conditions, including neurodegenerative disorders.
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