Automatic Sleep Stage Classification

11 papers with code • 2 benchmarks • 3 datasets

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Most implemented papers

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.

MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

IoBT-VISTEC/MetaSleepLearner 8 Apr 2020

This is the first work that investigated a non-conventional pre-training method, MAML, resulting in a possibility for human-machine collaboration in sleep stage classification and easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.

Automatic sleep stage classification with deep residual networks in a mixed-cohort setting

neergaard/deep-sleep-pytorch 21 Aug 2020

We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts.

RobustSleepNet: Transfer learning for automated sleep staging at scale

Dreem-Organization/RobustSleepNet 7 Jan 2021

Moreover, even when the PSG montage is compatible, publications have shown that automatic approaches perform poorly on unseen data with different demographics.

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

emadeldeen24/AttnSleep 28 Apr 2021

The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.

Time-Series Representation Learning via Temporal and Contextual Contrasting

emadeldeen24/TS-TCC 26 Jun 2021

In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

emadeldeen24/ADAST 9 Jul 2021

Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring

predict-idlab/sleep-linear 15 Jul 2022

We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models.