11 papers with code • 5 benchmarks • 2 datasets
The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases.
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.
Ranked #2 on Sleep Stage Detection on Sleep-EDF
Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders.
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
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.
Ranked #1 on Multimodal Sleep Stage Detection on Sleep-EDF-SC
We developed a framework to compare automated approaches to a consensus of multiple human scorers.
Ranked #1 on Sleep Stage Detection on DODH
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.
Ranked #2 on Sleep Stage Detection on MASS SS2
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.
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.
Ranked #1 on Sleep Stage Detection on MASS SS2