Sleep Stage Detection
20 papers with code • 16 benchmarks • 6 datasets
Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals
Benchmarks
These leaderboards are used to track progress in Sleep Stage Detection
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Sleep-EDF
MaSS
Montreal Archive of Sleep Studies
ISRUC-Sleep
Latest papers with no code
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
The results indicate that training the model on multimodal data does positively influence performance when tested on unimodal data.
Using Ballistocardiography for Sleep Stage Classification
A practical way of detecting sleep stages has become more necessary as we begin to learn about the vast effects that sleep has on people's lives.
Adaptive Memory Networks with Self-supervised Learning for Unsupervised Anomaly Detection
In this paper, we propose a novel approach called Adaptive Memory Network with Self-supervised Learning (AMSL) to address these challenges and enhance the generalization ability in unsupervised anomaly detection.
Learned Factor Graphs for Inference from Stationary Time Sequences
Learned factor graph can be realized using compact neural networks that are trainable using small training sets, or alternatively, be used to improve upon existing deep inference systems.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models.
Sleep stage classification from heart-rate variability using long short-term memory neural networks
A long short-term memory (LSTM) network is proposed as a solution to model long-term cardiac sleep architecture information and validated on a comprehensive data set (292 participants, 584 nights, 541. 214 annotated 30 s sleep segments) comprising a wide range of ages and pathological profiles, annotated according to the Rechtschaffen and Kales (R&K) annotation standard.
The Dreem Headband as an Alternative to Polysomnography for EEG Signal Acquisition and Sleep Staging
We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH’s automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring.
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
Sleep scoring is a necessary and time-consuming task in sleep studies.
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea.
