New method uses HDP-HMM and multitaper spectral estimation for automated sleep state classification.
problem Manual sleep scoring is subjective, time-consuming, and doesn't capture neural dynamics.
method Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) with multitaper spectral estimation.
result Automated algorithm recovers sleep dynamics and identifies subject-specific microstates.
Study uses CNNs to detect sleep arousals more accurately.
problem Accurate detection of sleep arousals for better sleep quality assessment.
method Investigated five 1D CNN models on EEG signals from PhysioNet/Computing in Cardiology Challenge 2018.
result Best 1D CNN model achieved high precision and recall metrics.
Deep learning detects sleep events in EEG, reducing expert dependency.
problem Manual EEG event annotation by sleep experts is time-consuming and variable.
method Convolutional neural network for joint event detection in EEG signals.
result Deep learning method outperforms existing event-specific algorithms.
Improved sleep apnea detection using sensor fusion and backward shortcut connections.
problem Untreated sleep apnea leads to severe health consequences; automated detection is needed.
method Late sensor fusion using backward shortcut connections to improve deep learning models.
result Significant improvement in predictive performance over single sensor methods.
RED detects sleep EEG events using deep neural networks, outperforming previous methods.
problem Manual detection of sleep EEG events is time-consuming and variable.
method Deep Recurrent Neural Networks (RNNs) with convolutional and recurrent components.
result RED outperforms state-of-the-art methods in sleep spindle and K-complex detection.
Study automates detection of visitation disruptions in ICU patients.
problem Difficulty in detecting frequent visitation disruptions in ICU patients.
method Used DensePose R-CNN model to count people in video frames, analyzed disruptions and patient outcomes.
result Automated method detects visitation disruptions, impacts on pain and length of stay examined.
DOSED detects sleep micro-architecture events in EEG signals.
problem Manual annotation of sleep micro-architecture events is time-consuming and prone to variability.
method DOSED is a deep learning architecture that jointly predicts event locations, durations, and types in EEG time series.
result DOSED outperforms current state-of-the-art detection methods on 4 datasets and 3 types of events (spindles, K-complexes, arousals).
Nonlinear filtering extracts relevant variables from multimodal sleep data.
problem Recover relevant variables from multiple sensor data.
method Diffusion-based manifold learning for nonlinear filtering.
result Method gives robust data-driven representation correlated with sleep process.
Survey of techniques for diagnosing pediatric sleep apnea from inexpensive data.
problem Diagnosing pediatric sleep apnea from limited and variable data.
method Exploratory data analysis using correlation networks, Mapper, SVD; supervised and unsupervised learning techniques.
result Analysis of various learning techniques applied to pediatric sleep apnea data.
Model tracks sleep stages from CPAP flow signals, improving accuracy.
problem Accurate sleep staging from CPAP flow signals for monitoring CPAP therapy.
method End-to-end framework combining deep convolution and recurrent neural networks with a structured output layer.
result Improved accuracy by 10% compared to previous methods.
VIPs use IPs for efficient inference in flexible models.
problem Efficient inference in flexible models like Bayesian neural networks and Gaussian processes.
method Variational Implicit Processes (VIPs) using generalised wake-sleep updates.
result VIPs provide better uncertainty estimates and lower errors compared to existing methods.
Study proposes automated framework for REM Sleep Behaviour Disorder detection.
problem Early detection of REM Sleep Behaviour Disorder (RBD) as a predictor of Parkinson's disease.
method Automated sleep staging followed by RBD identification using a Random Forest classifier and 156 features from EEG, EOG, and EMG channels.
result Automated RBD detection achieved 96% accuracy, surpassing individual established metrics.
Approach detects sleep arousal with high accuracy.
problem Detecting sleep arousals in polysomnographic recordings.
method Scattering Transform and Recurrent Neural Networks (RNN) for sequence learning.
result AUROC of 88.0% and AUPRC of 42.1% on 10% test set.
U-Time uses a fully convolutional network for sleep stage classification.
problem Challenges in tuning and optimizing recurrent neural networks for sleep data.
method U-Time is a fully feed-forward deep learning approach based on U-Net architecture.
result U-Time outperforms state-of-the-art models for sleep stage classification.
Study uses machine learning to detect sleep disorders by identifying brain patterns.
problem Detecting sleep disorders through EEG patterns in NREM sleep cycles.
method Feature engineering and machine learning model for predicting Cyclic Alternating Patterns (CAP).
result The model accurately predicts CAP sequences associated with sleep disorders.
Study tackles database variability in medical data using ensemble models and CNNs.
problem Achieving robust generalization in machine learning models across multiple medical databases.
method Ensemble of local models based on convolutional neural networks (CNNs) and various data preprocessing techniques.
result Improved inter-database generalization performance and scalability of models.
Deep learning detects sleep state fluctuations in neonates from single EEG channel.
problem Monitoring sleep state fluctuations in neonatal intensive care units.
method Deep learning-based algorithm trained on 53 EEG recordings, validated on 30 polysomnography recordings.
result High accuracy (90%) in detecting quiet sleep states from single EEG channel, generalizing well to external dataset.
Deep learning detects sleep events like arousals and leg movements.
problem Detecting arousals and leg movements in polysomnogram for sleep disorders.
method Deep learning model trained on 1,485 subjects, tested on 1,000 recordings.
result Optimal detection achieved with dynamic event window for arousals and static window for leg movements.
Deep transfer learning improves automatic sleep staging accuracy.
problem Small cohort data variability and inefficiency in sleep studies.
method Deep transfer learning approach using a large dataset to a small cohort.
result Significant performance improvement on automatic sleep staging.
A deep neural network detects sleep events in polysomnograms with high accuracy.
problem Manual scoring of sleep events in clinical analysis is inconsistent and time-consuming.
method A single deep neural network architecture trained on 1653 recordings for joint detection of arousals, leg movements, and sleep disordered breathing.
result Joint detection of sleep events yields higher accuracy compared to separate models, and correlates well with manual annotations.
Deep learning improves sleep apnea diagnosis accuracy.
problem Manual sleep expert scoring is tedious, time-consuming, and variable.
method Adapted deep learning method DOSED for automatic sleep event detection in PSG.
result Automatic approach achieved 81% accuracy for sleep apnea severity diagnosis.
SLEEPER combines deep learning with expert rules for accurate sleep staging.
problem Manual sleep staging is tedious and requires expert time.
method SLEEPER uses convolutional neural networks and expert rules to generate interpretable models.
result SLEEPER achieves comparable accuracy to human experts and deep neural networks.
Deep learning classifies sleep stages from EEG signals, aiding professionals.
problem Manual scoring of sleep stages from EEG signals is tedious and requires trained professionals.
method Multitaper spectral analysis and deep convolutional neural networks for automatic classification.
result System accurately classifies sleep stages in new patients, favorably compared to state-of-the-art.
Solves the Sleeping Beauty problem as a 'thirder' using the Kelly Criterion.
problem Solving the Sleeping Beauty problem with imperfect recall.
method Using the Kelly Criterion under multiplicative dynamics to maximize wealth growth rate.
result Sleeping Beauty maximizes expected growth rate as a 'thirder' and is impervious to Dutch books.
Study uses EDA data to monitor sleep, finds EDA Magnitude predicts SE changes.
problem Detecting small changes in sleep quality using EDA data.
method Factor analysis, causal model search, structural equation modeling, logistic regression, naive Bayes.
result EDA Magnitude is a strong predictor of self-reported sleep efficiency.
Bayesian approach predicts brain-age from EEG sleep states across age.
problem Inconsistent brain-age predictions due to subjective sleep state classification.
method Unified Bayesian Network with Gaussian Mixture Models for EEG sleep states.
result Improved accuracy in brain-age prediction over a wider age range.
CNNs score sleep stages from single EEG channel.
problem Automatically scoring sleep stages from EEG data.
method Convolutional Neural Networks (CNNs) trained on class-balanced data.
result High accuracy (81%) across sleep stages.
Framework uses CNNs for joint sleep stage classification and prediction.
problem Diagnosing and treating sleep disorders requires accurate sleep stage identification.
method Joint classification-and-prediction CNN framework based on a novel architecture.
result Framework achieves 82.3% and 83.6% accuracy on two public datasets.
Model captures sleep patterns in infant data.
problem Infant sleep data analysis with missing values.
method Nonparametric model using low-rank matrix factorization with time-smoothing regularization.
result Extracts meaningful sleep patterns and trends.
Study benchmarks automated sleep staging against human scorers, achieving human-level performance.
problem Lack of standardized comparison between human and automated sleep staging.
method Developed multi-scored datasets and a framework to compare multiple human scorers' consensus.
result Many automated methods can match human scorers' performance, with SimpleSleepNet achieving high F1 scores.
RobustSleepNet automates sleep stage classification for any PSG montage.
problem Manual sleep staging is tedious and expensive; automatic methods are limited by PSG montage and demographic differences.
method RobustSleepNet is a deep learning model that handles arbitrary PSG montages and is robust to demographic changes.
result RobustSleepNet achieves 97% F1 score on unseen data, outperforming specific training datasets by 2%.
A wearable device-based sleep stage classifier using feature learning and RNNs.
problem Automatic sleep stage classification using wearable devices.
method Multi-level feature learning framework and RNN classifier with BLSTM.
result The algorithm achieves high precision, recall, and F1 scores in both resting and comprehensive groups.
SeqSleepNet tackles automatic sleep staging as a sequence-to-sequence problem.
problem Automatic sleep staging as a sequence-to-sequence classification problem.
method End-to-end hierarchical recurrent neural network (SeqSleepNet) with filterbank and attention-based recurrent layers.
result SeqSleepNet achieves high accuracy (87.1% overall accuracy, 83.3% macro F1-score, 0.815 Cohen's kappa) on a publicly available dataset.
A neural network learns from examples and optimizes by dreaming.
problem The gap between training data and biological neural networks' experience.
method Inspired by biological learning, a generalized Hopfield network with Hebbian learning and off-line sleeping mechanisms.
result The network learns from examples, generalizes, and optimizes its storage capacity.
DeepSleepNet uses CNN and LSTM to score sleep stages from raw EEG data.
problem Automatic sleep stage scoring using raw EEG data.
method Deep learning model using CNN for time-invariant features and LSTM for transition rules.
result DeepSleepNet achieves similar accuracy to state-of-the-art methods on different EEG datasets.
Simple machine learning models outperform deep learning for sleep scoring.
problem Limited applicability and lack of interpretability of deep learning solutions for sleep scoring.
method Revisited sleep stage classification using classical machine learning, including preprocessing, feature extraction, and simple machine learning models.
result Competitive performance achieved with conventional machine learning models on public datasets.
New deep learning method validated across multiple sleep staging databases.
problem Improving automatic sleep scoring accuracy across different datasets.
method Ensemble of local models using deep learning for automatic sleep staging.
result Good general performance compared to human experts and state-of-the-art methods.
CNN improves OSA diagnosis accuracy from PSG data.
problem Manual PSG analysis by specialists is tedious, time-consuming, and prone to errors.
method 1D CNN architecture with convolutional and FCN layers for OSA severity classification.
result Proposed CNN model achieves excellent classification results without manual preprocessing.
Extends HMM to topological spaces for modeling complex data.
problem Modeling complex, continuous data in infinite-dimensional spaces.
method Use of Onsager-Machlup functional and Cameron-Martin space.
result Demonstrates versatility in identifying sleep states and snowfall patterns.
Deep transfer learning improves sleep staging with small datasets.
problem Insufficient data for deep neural networks due to channel mismatch.
method Transfer learning from large to small datasets, finetuning the pretrained network.
result Significant performance improvement in sleep staging.
Deep LSTM models predict weight objectives from sleep and step data.
problem Predicting weight objectives from multimodal time-series data.
method Design and optimise cross-modal LSTM architectures, including X-LSTM.
result X-LSTM improves prediction accuracy with fewer parameters.
Deep neural net improves sleep stage classification from raw EEG.
problem Improving accuracy of sleep stage classification from raw EEG data.
method 34-layer deep residual ConvNet architecture trained on raw single channel EEG data.
result Proposed network outperforms state-of-the-art methods in sleep staging accuracy.
Paper proposes an auto-encoder with selective attention for better sleep stage classification.
problem Challenges in automatic sleep staging, especially with large feature sets.
method Auto-encoder with selective attention mechanism for feature learning.
result Performance improvement over regular auto-encoder and DBN.
Develops a fast non-invasive tool for diagnosing pediatric sleep apnea.
problem Diagnosing pediatric obstructive sleep apnea using an overnight sleep study is often impractical.
method Combines persistent homology, geometric shape analysis, and convolutional neural networks to classify facial images.
result Facial features associated with obstructive sleep apnea can be recognized for diagnosis.
A wearable ear-EEG sensor monitors sleep patterns without patient involvement.
problem Monitoring sleep patterns without patient inconvenience or medical specialist involvement.
method Unobtrusive in-ear sensor for recording ear-EEG, using SEF and MSFE for classification.
result Achieved accuracies ranging from 78.5% to 95.2% for ear-EEG labels predicted from ear-EEG, and 76.8% to 91.8% for scalp-EEG labels predicted from ear-EEG.
Personalized sleep staging achieved with single-night data using KL-divergence regularization.
problem Improving automatic sleep staging accuracy with limited single-night data.
method KL-divergence regularization for transfer learning from a pretrained model to a personalized model.
result Personalized sleep staging accuracy of 79.6% with KL-divergence regularization.
Sleep-based regularization stabilizes STDP in recurrent neural networks.
problem Pathological weight dynamics in recurrent SNNs.
method Periodic offline phases with stochastic decay and spontaneous activity.
result Sleep-based renormalization prevents weight saturation and preserves learned structure.
Study optimizes deep learning models for sleep stage classification.
problem Time-consuming and inconsistent manual sleep stage scoring.
method Investigated architectural choices in encoder-predictor architectures for polysomnography recordings.
result Robust architectures improve sleep stage classification performance.