Sleep Stage Detection
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Benchmarks
SHHS
Sleep-EDF
MASS SS3
SHHS (single-channel)
DODO
Sleep-EDFx
DODH
ISRUC-Sleep
MASS (single-channel)
MASS SS2
PhysioNet Challenge 2018
Most implemented
DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach
Structure-Preserving Transformers for Sequences of SPD Matrices
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Papers
The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp
Sleep staging is essential for the assessment of sleep quality and the diagnosis of sleep-related disorders. Conventional polysomnography (PSG), while considered the gold standard, is intrusive, labor-intensive, and unsu…
Sleep Stage DetectionSleep QualityQuasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying Data
In this paper, we propose Quasi Zigzag Persistent Homology (QZPH) as a framework for analyzing time-varying data by integrating multiparameter persistence and zigzag persistence. To this end, we introduce a stable topolo…
Sleep Stage DetectionToward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Sleep is essential for maintaining human health and quality of life. Analyzing physiological signals during sleep is critical in assessing sleep quality and diagnosing sleep disorders. However, manual diagnoses by clinic…
Contrastive LearningDiagnosticEEGElectroencephalogram (EEG)+6MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
Sleep profoundly affects our health, and sleep deficiency or disorders can cause physical and mental problems. Despite significant findings from previous studies, challenges persist in optimizing deep learning models, es…
Contrastive LearningEEGSleep Stage DetectionHeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal Analysis
The HeartBert model is introduced with three primary objectives: reducing the need for labeled data, minimizing computational resources, and simultaneously improving performance in machine learning systems that analyze E…
Heartbeat ClassificationSelf-Supervised LearningSleep Stage DetectionMulti-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks
Objective. Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, a…
EEGMulti-Task LearningSleep QualitySleep Stage Detection