medical-6

Sleep Staging

3260 papers • 126 benchmarks • 313 datasets

Human Sleep Staging into W-R-N or W-R-L-D classes from multiple or single polysomnography signals

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Benchmarks

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Libraries

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Use these libraries to find sleep-staging-6 models and implementations

acrophase/sleep_staging_kd

3 papers 12

Datasets

Montreal Archive of Sleep Studies

Subtasks

EEG based sleep staging ECG based Sleep Staging W-R-L-D Sleep Staging W-R-N Sleep Staging

Most implemented papers

U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging

C. Igel, Mathias Perslev, M. Jensen, S. Darkner, P. Jennum•Wed Oct 23 2019

U-Time is a temporal fully convolutional network based on the U-Net architecture that was originally proposed for image segmentation developed for the analysis of sleep data and reaches or outperforms current state-of-the-art deep learning models while being much more robust in the training process and without requiring architecture or hyperparameter adaptation across tasks.

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SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

Fernando Andreotti, Huy Phan, Navin Cooray, O. Y. Chén, M. de Vos•Thu Sep 27 2018

This paper proposes a hierarchical recurrent neural network named SeqSleepNet that outperforms the state-of-the-art approaches, achieving an overall accuracy, macro F1-score, and Cohen’s kappa of 87.1%, 83.3%, and 0.815 on a publicly available dataset with 200 subjects.

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Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

P. Maes, Abhay Koushik, J. Amores•Sat Nov 24 2018

This work demonstrates an end-to-end on-smartphone pipeline that can infer sleep stages in just single 30-second epochs, with an overall accuracy of 83.5% on 20-fold cross validation for five-class classification of sleep stages using the open Sleep-EDF dataset.

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Dreem Open Datasets: Multi-Scored Sleep Datasets to Compare Human and Automated Sleep Staging

Antoine Guillot, F. Sauvet, E. During, Valentin Thorey•Wed Oct 30 2019

It is highlighted that state-of-the-art automated sleep staging outperforms human scorers performance for healthy volunteers and patients suffering from obstructive sleep apnea.

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Uncovering the structure of clinical EEG signals with self-supervised learning

Hubert J. Banville, Alexandre Gramfort, Omar Chehab, Aapo Hyvärinen, D. Engemann•Thu Jul 30 2020

The results suggest that self-supervision may pave the way to a wider use of deep learning models on EEG data, and linear classifiers trained on SSL-learned features consistently outperformed purely supervised deep neural networks in low-labeled data regimes while reaching competitive performance when all labels were available.

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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

Fernando Andreotti, Huy Phan, Navin Cooray, O. Y. Chén, M. de Vos•Tue May 15 2018

This paper proposes a joint classification-and-prediction framework based on convolutional neural networks (CNNs) for automatic sleep staging, and introduces a simple yet efficient CNN architecture to power the framework.

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Detection of REM sleep behaviour disorder by automated polysomnography analysis

Fernando Andreotti, M. Vos, Navin Cooray, Christine Lo, M. Symmonds, M. Hu•Sun Nov 11 2018

This study validates a tractable, fully-automated, and sensitive pipeline for RBD identification that could be translated to wearable take-home technology and demonstrates that incorporating sleep architecture and sleep stage transitions can benefit RBD detection.

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End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

Ahmed Imtiaz Humayun, Asif Sushmit, T. Hasan, M. Bhuiyan•Mon Apr 22 2019

A 34-layer deep residual ConvNet architecture for end-to-end sleep staging is proposed, which takes raw single channel electroencephalogram signal as input and yields hypnogram annotations for each 30s segments as output.

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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Huy Phan, O. Y. Chén, M. de Vos, P. Koch, Zongqing Lu, I. Mcloughlin, A. Mertins•Mon Jul 29 2019

A deep transfer learning approach to overcome data-variability and data-inefficiency issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging would enable one to improve the quality of automaticsleep staging models when the amount of data is relatively small.

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Screening for REM Sleep Behaviour Disorder with Minimal Sensors

Fernando Andreotti, M. Vos, Navin Cooray, Christine Lo, M. Symmonds, M. Hu•Wed Oct 23 2019

This study demonstrated the feasibility of using signals from a single EOG and EMG sensor to detect RBD using fully-automated techniques and proposes a cost-effective, practical, and simple RBD identification support tool using only two sensors (EMG and EOG), ideal for screening purposes.

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