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.
Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to moni…
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 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.
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%.
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.
Deep neural nets classify sleep stages from raw PSG data with high accuracy.
problem Automatically classifying sleep stages from raw polysomnogram signals.
method Deep residual neural networks trained on 50 convolutional layers.
result Best model achieved 84.1% accuracy and 0.746 Cohen's kappa.
Sleep studies are important for diagnosing sleep disorders such as insomnia, narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw polisomnography signals, which is a tedious visual task requiring the workload of highly trained professionals. Consequently, research efforts to purse for an auto…
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.
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.
We used convolutional neural networks (CNNs) for automatic sleep stage scoring based on single-channel electroencephalography (EEG) to learn task-specific filters for classification without using prior domain knowledge. We used an openly available dataset from 20 healthy young adults for evaluation and applied 20-fold …
The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features which require prior knowledge of sleep analysis. Only a few of them encode the temporal information such as transition ru…
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.
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.
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.
Electroencephalographic (EEG) monitoring of neural activity is widely used for sleep disorder diagnostics and research. The standard of care is to manually classify 30-second epochs of EEG time-domain traces into 5 discrete sleep stages. Unfortunately, this scoring process is subjective and time-consuming, and the defi…
Automatic sleep staging is a challenging problem and state-of-the-art algorithms have not yet reached satisfactory performance to be used instead of manual scoring by a sleep technician. Much research has been done to find good feature representations that extract the useful information to correctly classify each epoch…
Correctly identifying sleep stages is important in diagnosing and treating sleep disorders. This work proposes a joint classification-and-prediction framework based on CNNs for automatic sleep staging, and, subsequently, introduces a simple yet efficient CNN architecture to power the framework. Given a single input epo…
This paper proposes a practical approach for automatic sleep stage classification based on a multi-level feature learning framework and Recurrent Neural Network (RNN) classifier using heart rate and wrist actigraphy derived from a wearable device. The feature learning framework is designed to extract low- and mid-level…
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.
Preterm newborns undergo various stresses that may materialize as learning problems at school-age. Sleep staging of the Electroencephalogram (EEG), followed by prediction of their brain-age from these sleep states can quantify deviations from normal brain development early (when compared to the known age). Current auto…
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.
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.
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardio…
Deep neural networks improve sleep stage classification across diverse datasets.
problem Manual sleep scoring is subjective and lacks reliability; automatic systems generalize poorly.
method Developed a deep neural network using 15,684 polysomnography studies from five cohorts.
result Classification accuracy improved with more training data and multiple data sources.
Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classificat…
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.
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.
Paper characterizes early-stage dementia signatures from sensor data.
problem Detecting early-stage dementia from sensor data.
method Developed bespoke behavioural models from longitudinal sensor data.
result Found subtle differences in sleep quality and wandering between dementia patients and controls.
SleepNet detects sleep disorders using neural networks trained on PSG data.
problem Automated detection of sleep disorders like apnea and hypopnea.
method Dense Convolutional Neural Network (DRCNN) trained on Polysomnography (PSG) data.
result SleepNet achieved the first place in a sleep arousal detection challenge.
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.
Electroencephalography (EEG) during sleep is used by clinicians to evaluate various neurological disorders. In sleep medicine, it is relevant to detect macro-events (> 10s) such as sleep stages, and micro-events (<2s) such as spindles and K-complexes. Annotations of such events require a trained sleep expert, a time co…
Landmark AD improves AD's efficiency without sacrificing performance.
problem Computational burden in diffusion-based sensor fusion.
method Inspired by landmark diffusion, proposes Landmark AD.
result Landmark AD offers superior computational efficiency.
IITNet learns sleep stages from raw EEG using sub-epoch features and temporal contexts.
problem Automatic sleep scoring from raw single-channel EEG.
method IITNet uses a residual neural network to extract sub-epoch features and bidirectional LSTM to capture intra- and inter-epoch temporal contexts.
result IITNet achieves comparable performance to state-of-the-art methods, especially with longer sequence lengths.
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.
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).
The monitoring of sleep patterns without patient's inconvenience or involvement of a medical specialist is a clinical question of significant importance. To this end, we propose an automatic sleep stage monitoring system based on an affordable, unobtrusive, discreet, and long-term wearable in-ear sensor for recording t…
Fluctuations in heart rate are intimately tied to changes in the physiological state of the organism. We examine and exploit this relationship by classifying a human subject's wake/sleep status using his instantaneous heart rate (IHR) series. We use a convolutional neural network (CNN) to build features from the IHR se…
The problem of information fusion from multiple data-sets acquired by multimodal sensors has drawn significant research attention over the years. In this paper, we focus on a particular problem setting consisting of a physical phenomenon or a system of interest observed by multiple sensors. We assume that all sensors m…
Learned factor graphs improve inference from time sequences using neural networks.
problem Inference from time sequences with limited labeled data.
method Combines model-based algorithms and data-driven ML tools for stationary time sequences.
result Learned factor graphs can accurately infer from small training sets.
REST improves robustness and efficiency of sleep monitoring models.
problem Noise and energy efficiency in deep learning models for home health monitoring.
method Adversarial training and spectral/sparsity regularization.
result REST models achieve 19x parameter reduction and 15x MFLOPS reduction with 17x energy reduction and 9x faster inference.
Deep learning detects microsleep episodes in EEG data.
problem Automatic detection of microsleep episodes (MSEs) in EEG data.
method Convolutional neural networks (CNNs) and LSTM networks were implemented to analyze MWT data.
result Deep learning algorithms showed good performance close to human experts in detecting MSEs.
New methods for predicting compositional data using conformal prediction.
problem No well-established methods for constructing valid prediction sets in compositional data.
method Investigated three conformal prediction-based approaches for Dirichlet regression models.
result HDR approximation approach is robust in terms of coverage, while grid discretization reduces overcoverage.
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.
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.
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.