Sleep Stage Detection

17 papers with code • 15 benchmarks • 6 datasets

Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals

Most implemented papers

DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

akaraspt/deepsleepnet 12 Mar 2017

This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

SajadMo/SleepEEGNet 5 Mar 2019

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.

An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave

MIT-LCP/wfdb-python Journal of Open Research Software 2014

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.

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.

Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG

gist-ailab/IITNet-official 18 Feb 2019

A deep learning model, named IITNet, is proposed to learn intra- and inter-epoch temporal contexts from raw single-channel EEG for automatic sleep scoring.

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.

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.

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.

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

duggalrahul/REST 29 Jan 2020

By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference.