Sleep Staging

34 papers with code • 0 benchmarks • 1 datasets

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

Most implemented papers

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

perslev/U-Time NeurIPS 2019

We propose U-Time, a fully feed-forward deep learning approach to physiological time series segmentation developed for the analysis of sleep data.

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

pquochuy/SeqSleepNet 28 Sep 2018

At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.

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

kylemath/DeepEEG 25 Nov 2018

We present the first real-time sleep staging system that uses deep learning without the need for servers in a smartphone application for a wearable EEG.

Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

Dreem-Organization/dreem-learning-open 31 Oct 2019

We developed a framework to compare automated approaches to a consensus of multiple human scorers.

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

zacharycbrown/ssl_baselines_for_biosignal_feature_extraction 31 Jul 2020

Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.

Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

pquochuy/MultitaskSleepNet 16 May 2018

While the proposed framework is orthogonal to the widely adopted classification schemes, which take one or multiple epochs as contextual inputs and produce a single classification decision on the target epoch, we demonstrate its advantages in several ways.

Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

navsnav/RBD-Sleep-Detection 12 Nov 2018

This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.

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

mHealthBuet/ASSC 23 Apr 2019

The experimental results demonstrate the superiority of the proposed network compared to the best existing method, providing a relative improvement in epoch-wise average accuracy of 6. 8% and 6. 3% on the household data and multi-source data, respectively.

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

pquochuy/sleep_transfer_learning 30 Jul 2019

We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains: the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database.

Screening for REM Sleep Behaviour Disorder with Minimal Sensors

navsnav/Minimal-RBD-Detection 24 Oct 2019

This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.