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Sleep Staging

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Human Sleep Staging into W-R-N or W-R-L-D classes from multiple or single polysomnography signals

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Dreem-Organization/RobustSleepNet

공식 코드

RobustSleepNet: Transfer learning for automated sleep staging at scale

https://github.com/Dreem-Organization/dreem-learning-evaluation

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Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

https://github.com/Dreem-Organization/dreem-learning-open

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Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

https://github.com/HuayuLiArizona/Conformalized-Multiple-Instance-Learning-For-MedTS

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Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized Models

https://github.com/IoBT-VISTEC/MetaSleepLearner

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

https://github.com/Jeongseungwoo/Efficient-Continuous-Manifold-Learning

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Deep Efficient Continuous Manifold Learning for Time Series Modeling

https://github.com/Mithunjha/EarEEG_KnowledgeDistillation

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A Knowledge Distillation Framework For Enhancing Ear-EEG Based Sleep Staging With Scalp-EEG Data

https://github.com/Niloy333/eegFloss

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eegFloss: A Python package for refining sleep EEG recordings using machine learning models

https://github.com/acrophase/sleep_staging_kd

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A Deep Knowledge Distillation framework for EEG assisted enhancement of single-lead ECG based sleep staging

https://github.com/ai4healthuol/s4sleep

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S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification models

https://github.com/biomedical-signal-processing/sleepyland

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SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models

https://github.com/dslaborg/frequency-pretraining

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Data-Efficient Sleep Staging with Synthetic Time Series Pretraining

https://github.com/dslaborg/sumov2

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From Sleep Staging to Spindle Detection: Evaluating End-to-End Automated Sleep Analysis

https://github.com/emadeldeen24/ADAST

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

https://github.com/hsd1503/DL-ECG-Review

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Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review

https://github.com/jathurshan0330/cross-modal-transformer

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Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers

https://github.com/jukiecheung/mvitime

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Contrastive Learning for Sleep Staging based on Inter Subject Correlation

https://github.com/komdec/detectsleepnet

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An Interpretable and Efficient Sleep Staging Algorithm: DetectsleepNet

https://github.com/ku-milab/transsleep

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TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging

https://github.com/likith012/muleeg

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mulEEG: A Multi-View Representation Learning on EEG Signals

https://github.com/mHealthBuet/ASSC

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End-to-end Sleep Staging with Raw Single Channel EEG using Deep Residual ConvNets

https://github.com/majy-yuji/st-usleepnet

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ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging

https://github.com/mathieuseraphim/spdtransnet

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Structure-Preserving Transformers for Sequences of SPD Matrices

https://github.com/navsnav/RBD-Sleep-Detection

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Detection of REM Sleep Behaviour Disorder by Automated Polysomnography Analysis

https://github.com/pquochuy/MultitaskSleepNet

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Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification

https://github.com/pquochuy/SeqSleepNet

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SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging

https://github.com/pquochuy/l-seqsleepnet

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L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging

https://github.com/pquochuy/sleep_transfer_learning

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Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

https://github.com/predict-idlab/sleep-linear

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Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring

https://github.com/qhan-hu/multi-view-fusion-and-missing-in-cardiovascular-signals

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Efficient Multi-View Fusion and Flexible Adaptation to View Missing in Cardiovascular System Signals

https://github.com/sczzz3/SDI

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Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health

https://github.com/shashankmanjunath/tda_sleep_staging

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Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves

https://github.com/tsy935/graphs4mer

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Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models

https://github.com/wjq-learning/sleepdg

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Generalizable Sleep Staging via Multi-Level Domain Alignment

https://github.com/xuejiang16/ssl-torch

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Self-supervised Contrastive Learning for EEG-based Sleep Staging

https://github.com/ycq091044/contrawr

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Self-supervised EEG Representation Learning for Automatic Sleep Staging

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