Automatic Sleep Stage Classification
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Benchmarks
Sleep-EDF
ISRUC-Sleep
Most implemented
Time-Series Representation Learning via Temporal and Contextual Contrasting
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
Contrastive Learning for Sleep Staging based on Inter Subject Correlation
Papers
NanoSleep: A Parameter-Efficient Hybrid Temporal Convolutional Network for Single-Channel Sleep Stage Classification
Sleep stage classification from single-channel electroencephalography (EEG) is essential for wearable and home-based sleep monitoring. However, many deep learning models achieve high accuracy at the cost of large model s…
Automatic Sleep Stage ClassificationStaging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
Automated sleep staging is commonly approached as a supervised machine learning problem, with deep learning methods dominating recent research. While machine learning models achieve near-human level agreement with human-…
Automatic Sleep Stage ClassificationMSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
Recent advancements in machine learning-based signal analysis, coupled with open data initiatives, have fuelled efforts in automatic sleep stage classification. Despite the proliferation of classification models, few hav…
Automatic Sleep Stage ClassificationBiT-MamSleep: Bidirectional Temporal Mamba for EEG Sleep Staging
In this paper, we address the challenges in automatic sleep stage classification, particularly the high computational cost, inadequate modeling of bidirectional temporal dependencies, and class imbalance issues faced by …
Automatic Sleep Stage ClassificationClassificationEEGFeature Importance+2Evaluating sleep-stage classification: how age and early-late sleep affects classification performance
Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. …
Automatic Sleep Stage ClassificationClassificationSleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stages
Deep neural networks have played an important role in automatic sleep stage classification because of their strong representation and in-model feature transformation abilities. However, class imbalance and individual het…
Automatic Sleep Stage ClassificationClassificationData AugmentationEEG+3