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Found 5 papers, 4 papers with code
Automated Movement Detection with Dirichlet Process Mixture Models and Electromyography
This study proposes a framework for automated limb-movement detection by fusing data from two EMG sensors (from the left and right limb) through a Dirichlet process mixture model.
Screening for REM Sleep Behaviour Disorder with Minimal Sensors
This study investigates a minimal set of sensors to achieve effective screening for RBD in the population, integrating automated sleep staging (three state) followed by RBD detection without the need for cumbersome electroencephalogram (EEG) sensors.
Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis
This study also achieved automated sleep staging with a level of accuracy comparable to manual annotation.
SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging
At the sequence processing level, a recurrent layer placed on top of the learned epoch-wise features for long-term modelling of sequential epochs.
Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
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
