ArticleSleep & breathing = Schlaf & Atmung2020
Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data.
Article in Sleep & breathing = Schlaf & Atmung, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Unconstrained deep learning-based sleep stage classification using cardiorespiratory and body movement activities in adults with suspected sleep apnea.Proceedings of the Japan Academy. Series B, Physical and biological sciences · 2025Article
- Automated sleep staging from single-channel electroencephalogram using hybrid neural network with manual features and attention.iScience · 2025Article
- Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.Scientific reports · 2023Article
- [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
- An End-to-End Multi-Channel Convolutional Bi-LSTM Network for Automatic Sleep Stage Detection.Sensors (Basel, Switzerland) · 2023Article
- Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.Sleep & breathing = Schlaf & Atmung · 2023Review
- A novel automated robust dual-channel EEG-based sleep scoring system using optimal half-band pair linear-phase biorthogonal wavelet filter bank.Applied intelligence (Dordrecht, Netherlands) · 2023Article
- DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification.Frontiers in physiology · 2023Article
- Cross-Modal Contrastive Hashing Retrieval for Infrared Video and EEG.Sensors (Basel, Switzerland) · 2022Article
- Automatic sleep stages classification using multi-level fusion.Biomedical engineering letters · 2022Article
- A Holistic Strategy for Classification of Sleep Stages with EEG.Sensors (Basel, Switzerland) · 2022Article
- Automatic and Accurate Sleep Stage Classification via a Convolutional Deep Neural Network and Nanomembrane Electrodes.Biosensors · 2022Article
- Obstructive Sleep Apnea Detection Based on Sleep Sounds via Deep Learning.Nature and science of sleep · 2022Article
- Confidence-Based Framework Using Deep Learning for Automated Sleep Stage Scoring.Nature and science of sleep · 2021Article
- Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network.Nature and science of sleep · 2021Article
- Machine and Deep Learning in Molecular and Genetic Aspects of Sleep Research.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2021Review
- Multi-Branch Convolutional Neural Network for Automatic Sleep Stage Classification with Embedded Stage Refinement and Residual Attention Channel Fusion.Sensors (Basel, Switzerland) · 2020Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop an automated framework for sleep stage scoring from PSG via a deep neural network.
methodsAn automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa.
resultsTwo hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's Kappa of 0.7276. Top-2 accuracy (the proportion of test samples for which the true label is among the two most probable labels given by the model) was significantly improved compared to the overall classification accuracy, with the average being 0.9602. The number of arousals affected the model's performance.
conclusionThis research provides a robust and reliable model with the inter-rater agreement nearing that of human experts. Determining the most appropriate evaluation parameters for sleep staging is a direction for future research.
Indexed as
Identifiers
What OpenQuestion holds
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.