ArticleSleep2020
Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.
Article in Sleep, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Sensing Technologies for Wearable Sleep Staging.Sensors (Basel, Switzerland) · 2021Pooled it
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- Late-onset unexplained epilepsy as a risk factor for cognitive impairment and dementia: Protocol for a multi-center prospective longitudinal observational study (ELUCID).Epilepsia open · 2026Observational
- Long-term forgetting, sleep, and tau in autosomal-dominant Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Association of Sleep Spindle Activity With Cognitive Decline in Early Clinical Stages of Alzheimer Disease.Neurology · 2026Article
- Phase precession of spindle-slow oscillation coupling across the human brain.bioRxiv : the preprint server for biology · 2025Article
- Sleep EEG and respiratory biomarkers of sudden unexpected death in epilepsy (SUDEP): a case-control study.The Lancet. Neurology · 2025Article
- Hyperarousal features in the sleep architecture of individuals with and without insomnia.Journal of sleep research · 2025Article
- Sleep functional connectivity, hyperexcitability, and cognition in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Epilepsy is associated with the accelerated aging of brain activity in sleep.Frontiers in physiology · 2024Article
- The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond.Frontiers in human neuroscience · 2024Review
- Explainable AI-driven model for gastrointestinal cancer classification.Frontiers in medicine · 2024Article
- Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data.Journal of personalized medicine · 2023Review
- Altered Sleep Microarchitecture and Cognitive Impairment in Patients With Temporal Lobe Epilepsy.Neurology · 2023Article
- Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring.Sleep · 2023Article
- Article
- Automatic sleep staging of single-channel EEG based on domain adversarial neural networks and domain self-attention.Frontiers in neuroscience · 2023Article
- Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing.Frontiers in sleep · 2023Review
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Authors and funding
8 authors.
Funding
Abstract
STUDY
objectivesDevelop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.
methodsUsing a clinical dataset of polysomnograms from 6,431 patients (MGH-PSG dataset), we trained a deep neural network to classify sleep stages based on scalp EEG data. The algorithm consists of a convolutional neural network for feature extraction, followed by a recurrent neural network that extracts temporal dependencies of sleep stages. The algorithm's inputs are four scalp EEG bipolar channels (F3-C3, C3-O1, F4-C4, and C4-O2), which can be derived from any standard PSG or scalp EEG recording. We initially trained the algorithm on the MGH-PSG dataset and used transfer learning to fine-tune it on a dataset of long-term (24-72 h) scalp EEG recordings from 112 patients (scalpEEG dataset).
resultsThe algorithm achieved a Cohen's kappa of 0.74 on the MGH-PSG holdout testing set and cross-validated Cohen's kappa of 0.78 after optimization on the scalpEEG dataset. The algorithm also performed well on two publicly available PSG datasets, demonstrating high generalizability. Performance on all datasets was comparable to the inter-rater agreement of human sleep staging experts (Cohen's kappa ~ 0.75 ± 0.11). The algorithm's performance on long-term scalp EEGs was robust over a wide age range and across common EEG background abnormalities.
conclusionWe developed a deep learning algorithm that achieves human expert level sleep staging performance on long-term scalp EEG recordings. This algorithm, which we have made publicly available, greatly facilitates the use of large long-term EEG clinical datasets for sleep-related research.
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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.