medical-6

Multimodal Sleep Stage Detection

3260 papers • 126 benchmarks • 313 datasets

Using multiple modalities such as EEG+EOG, EEG+HR instead of just relying on EEG (polysomnography)

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Benchmarks

These leaderboards are used to track progress in multimodal-sleep-stage-detection-6

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Dataset

Best Model

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Sleep-EDF-SC

Sleep-EDF-ST

Surrey-cEEGGrid

Libraries

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Use these libraries to find multimodal-sleep-stage-detection-6 models and implementations

Datasets

Sleep-EDF

Subtasks

No subtasks available.

Most implemented papers

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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Content

Surrey-cEEGGrid

Surrey-PSG

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Paper Graph

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

Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring

Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost, N. Vandenbussche, Michael Rademaker, Gilles Vandewiele, Sofie van Hoecke•Thu Jul 14 2022

It is shown 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, which opens the door to clinical adoption.

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Paper Graph

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