arXiv research

Study uses EDA data to monitor sleep, finds EDA Magnitude predicts SE changes.

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…

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…

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…

New algorithm reduces sleeping bandits' regret to O(sqrt(T)).

NRWS improves training of SBNs and HMs using natural gradient.

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…

REST improves robustness and efficiency of sleep monitoring models.

Efficient algorithms for online learning with changing action sets, achieving no-approximate-regret guarantees.

Landmark AD improves AD's efficiency without sacrificing performance.

Algorithm improves movie recommendation efficiency with fairness constraints.

Deep learning detects sleep state fluctuations in neonates from single EEG channel.

Deep learning detects sleep events like arousals and leg movements.

Deep transfer learning improves automatic sleep staging accuracy.

Evidence suggests Rapid-Eye-Movement (REM) Sleep Behaviour Disorder (RBD) is an early predictor of Parkinson's disease. This study proposes a fully-automated framework for RBD detection consisting of automated sleep staging followed by RBD identification. Analysis was assessed using a limited polysomnography montage fr…

A deep neural network detects sleep events in polysomnograms with high accuracy.

Deep learning improves sleep apnea diagnosis accuracy.

We study a variant of decision-theoretic online learning in which the set of experts that are available to Learner can shrink over time. This is a restricted version of the well-studied sleeping experts problem, itself a generalization of the fundamental game of prediction with expert advice. Similar to many works in t…

SLEEPER combines deep learning with expert rules for accurate sleep staging.

IITNet learns sleep stages from raw EEG using sub-epoch features and temporal contexts.

Solves the Sleeping Beauty problem as a 'thirder' using the Kelly Criterion.

Study uses CNNs to detect sleep arousals more accurately.

Model captures sleep patterns in infant data.

Study benchmarks automated sleep staging against human scorers, achieving human-level performance.

RobustSleepNet automates sleep stage classification for any PSG montage.

Electroencephalography (EEG) is a method to record the electrical signals in the brain. Recognizing the EEG patterns in the sleeping brain gives insights into the understanding of sleeping disorders. The dataset under consideration contains EEG data points associated with various physiological conditions. This study at…

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…

Simple machine learning models outperform deep learning for sleep scoring.

RED detects sleep EEG events using deep neural networks, outperforming previous methods.

New deep learning method validated across multiple sleep staging databases.

Improved sleep apnea detection using sensor fusion and backward shortcut connections.

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…

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…

CNN improves OSA diagnosis accuracy from PSG data.

Deep transfer learning improves sleep staging with small datasets.

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…

Deep neural net improves sleep stage classification from raw EEG.

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 …

Develops a fast non-invasive tool for diagnosing pediatric sleep apnea.

Personalized sleep staging achieved with single-night data using KL-divergence regularization.

Sleep-based regularization stabilizes STDP in recurrent neural networks.

Study automates detection of visitation disruptions in ICU patients.

Study optimizes deep learning models for sleep stage classification.

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…

SleepNet detects sleep disorders using neural networks trained on PSG data.

Background: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30s window of signal. For diagnosis, they also rely on shorter prototypical mic…

Near-optimal per-action regret bounds for sleeping bandits are derived.

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…