AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Sleep

Showing 241 to 250 of 269 articles

Clear Filters

Sleep staging from electrocardiography and respiration with deep learning.

Sleep
STUDY OBJECTIVES: Sleep is reflected not only in the electroencephalogram but also in heart rhythms and breathing patterns. We hypothesized that it is possible to accurately stage sleep based on the electrocardiogram (ECG) and respiratory signals.

Temporal dependency in automatic sleep scoring via deep learning based architectures: An empirical study.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The present study evaluates how effectively a deep learning based sleep scoring system does encode the temporal dependency from raw polysomnography signals. An exhaustive range of neural networks, including state of the art architecture, have been us...

TinySleepNet: An Efficient Deep Learning Model for Sleep Stage Scoring based on Raw Single-Channel EEG.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning has become popular for automatic sleep stage scoring due to its capability to extract useful features from raw signals. Most of the existing models, however, have been overengineered to consist of many layers or have introduced addition...

Automatic Sleep Stage Classification using Marginal Hilbert Spectrum Features and a Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
In this paper, we propose a novel method of automatic sleep stage classification based on single-channel electroencephalography (EEG). First, we use marginal Hilbert spectrum (MHS) to depict time-frequency domain features of five sleep stages of 30-s...

Automatic Detection of Respiratory Effort Related Arousals With Deep Neural Networks From Polysomnographic Recordings.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Sleep disorders have become more common due to the modern lifestyle and stress. The most severe case of sleep disorders called apnea is characterized by a complete breaking block, leading to awakening and subsequent sleep disturbances. The automatic ...

Predicting Age with Deep Neural Networks from Polysomnograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The aim of this study was to design a new deep learning framework for end-to-end processing of polysomnograms. This framework can be trained to analyze whole-night polysomnograms without the limitations of and bias towards clinical scoring guidelines...

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.

Anesthesia and analgesia
BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validat...

Artificial intelligence in sleep medicine: background and implications for clinicians.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Polysomnography remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources o...

Artificial intelligence in sleep medicine: an American Academy of Sleep Medicine position statement.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Sleep medicine is well positioned to benefit from advances that use big data to create artificially intelligent computer programs. One obvious initial application in the sleep disorders center is the assisted (or enhanced) scoring of sleep and associ...

Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison With Other Risk Factors.

The journals of gerontology. Series A, Biological sciences and medical sciences
BACKGROUND: Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (i) establish the predictive abi...