ArticleFrontiers in neuroscience2018
Automatic Human Sleep Stage Scoring Using Deep Neural Networks.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Multichannel Polysomnographic Sensor Fusion for Automatic Sleep Stage Classification.Sensors (Basel, Switzerland) · 2026Article
- Article
- A perspective on automated rapid eye movement sleep assessment.Journal of sleep research · 2025Review
- Article
- MLS-Net: An Automatic Sleep Stage Classifier Utilizing Multimodal Physiological Signals in Mice.Biosensors · 2024Article
- ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.Scientific reports · 2024Article
- Somnotate: A probabilistic sleep stage classifier for studying vigilance state transitions.PLoS computational biology · 2024Article
- Development of generalizable automatic sleep staging using heart rate and movement based on large databases.Biomedical engineering letters · 2023Article
- Looking for a balance between visual and automatic sleep scoring.NPJ digital medicine · 2023Article
- Automated analysis of a large-scale paediatric dataset illustrates the interdependent relationship between epilepsy and sleep.Scientific reports · 2023Article
- Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording.Cell reports methods · 2023Article
- Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring.Sleep · 2023Article
- U-Sleep's resilience to AASM guidelines.NPJ digital medicine · 2023Article
- Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.Sleep & breathing = Schlaf & Atmung · 2023Review
- Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.Frontiers in neuroscience · 2023Article
- Local field potentials and single unit dynamics in motor cortex of unconstrained macaques during different behavioral states.Frontiers in neuroscience · 2023Article
- Methodologies and Wearable Devices to Monitor Biophysical Parameters Related to Sleep Dysfunctions: An Overview.Micromachines · 2022Review
- Evaluation of Sleep Disturbances in Patients with Nocturnal Epileptic Seizures in a Romanian Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2022Article
- Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes.Biosensors · 2022Article
- Automated Scoring of Sleep and Associated Events.Advances in experimental medicine and biology · 2022Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The classification of sleep stages is the first and an important step in the quantitative analysis of polysomnographic recordings. Sleep stage scoring relies heavily on visual pattern recognition by a human expert and is time consuming and subjective. Thus, there is a need for automatic classification. In this work we developed machine learning algorithms for sleep classification: random forest (RF) classification based on features and artificial neural networks (ANNs) working both with features and raw data. We tested our methods in healthy subjects and in patients. Most algorithms yielded good results comparable to human interrater agreement. Our study revealed that deep neural networks (DNNs) working with raw data performed better than feature-based methods. We also demonstrated that taking the local temporal structure of sleep into account a priori is important. Our results demonstrate the utility of neural network architectures for the classification of sleep.
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Registered trials
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