medical-8

Sleep Stage Detection

3260 papers • 126 benchmarks • 313 datasets

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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in sleep-stage-detection-8

Trend

Dataset

Best Model

Actions

Sleep-EDF

MASS SS3

SHHS

Libraries

i

Use these libraries to find sleep-stage-detection-8 models and implementations

Datasets

Sleep-EDF

MaSS

Montreal Archive of Sleep Studies

SHHS

ISRUC-Sleep

PhysioNet Challenge 2018

Subtasks

Sleep Staging

Most implemented papers

DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG

Yike Guo, Akara Supratak, Hao Dong, Chao Wu•Sat Mar 11 2017

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG, and utilizes convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs.

1119

Content

DODO

MASS SS2

DODH

Montreal Archive of Sleep Studies

Sleep-EDFx (single-channel)

SHHS (single-channel)

Sleep-EDF (single-channel)

MASS (single-channel)

PhysioNet Challenge 2018 (single-channel)

Sleep-EDFx

PhysioNet Challenge 2018

ISRUC-Sleep

0

Paper Graph

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.

502 0

Paper Graph

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach

Sajad Mousavi, Fatemeh Afghah, U. R. Acharya•Mon Mar 04 2019

An automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores.

443 0

Paper Graph

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

Ikaro Silva, George B. Moody•Tue Sep 23 2014

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet’s software and databases and should easily be able to reproduce, validate, and compare results published based on Physio net's software and database.

207 0

Paper Graph

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.

18 0

Paper Graph

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.

126 0

Paper Graph

Intra- and inter-epoch temporal context network (IITNet) using sub-epoch features for automatic sleep scoring on raw single-channel EEG

Seunghyeok Back, Kyoobin Lee, H. Seo, Seongju Lee, Deokhwan Park, Tae Kim•Fri Jul 31 2020

The results support that considering the latest two-minute raw single-channel EEG can be a reasonable choice for sleep scoring via deep neural networks with efficiency and reliability and that introducing intra-epoch temporal context learning with a deep residual network contributes to the improvement in the overall performance.

195 0

Paper Graph

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.

387 0

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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.

136 0

Paper Graph

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

Cao Xiao, Jimeng Sun, Duen Horng Chau, Scott Freitas, Rahul Duggal•Tue Jan 28 2020

It is demonstrated that Rest produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise and quantitatively observes that Rest allows models to achieve up to 17 × energy reduction and 9 × faster inference.

23 0

Paper Graph

Adding a benchmark result helps the community track progress.