AIMC Topic: Sleep Stages

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Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population.

Sleep Sep 14, 2020
STUDY OBJECTIVES: To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients.
Neural Networks, Computer Algorithms Humans Polysomnography Sleep Stages Heart Rate Sleep
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Sleep staging from electrocardiography and respiration with deep learning.

Sleep Jul 13, 2020
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.
Sleep Stages Deep Learning Electrocardiography Sleep Respiration
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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 Jul 1, 2020
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...
Sleep Stages Deep Learning Electroencephalography Sleep Polysomnography
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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 Jul 1, 2020
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...
Deep Learning Electroencephalography Humans Sleep Stages Sleep Mental Recall
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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 Jul 1, 2020
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...
Humans Sleep Neural Networks, Computer Sleep Stages Electroencephalography
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Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.

Sleep Oct 21, 2019
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. ...
Electrooculography Sleep Stages Electromyography Polysomnography Neural Networks, Computer Reproducibility of Results Deep Learning Electroencephalography Humans
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Automatic Sleep Stages Classification Combining Semantic Representation and Dynamic Expert System.

Studies in health technology and informatics Aug 21, 2019
Interest in sleep has been growing in the last decades, considering its benefits for well-being, but also to diagnose sleep troubles. The gold standard to monitor sleep consists of recording the course of many physiological parameters during a whole ...
Humans Sleep Stages Semantics Electroencephalography Polysomnography Expert Systems
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Deep Learning with Convolutional Neural Network for detecting microsleep states from EEG: A comparison between the oversampling technique and cost-based learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Any occupation which involves critical decision making in real-time requires attention and concentration. When repetitive and expanded working periods are encountered, it can result in microsleeps. Microsleeps are complete lapses in which a subject i...
Neural Networks, Computer Deep Learning Humans Sleep Stages Electroencephalography Attention
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Fusion of End-to-End Deep Learning Models for Sequence-to-Sequence Sleep Staging.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Sleep staging, a process of identifying the sleep stages associated with polysomnography (PSG) epochs, plays an important role in sleep monitoring and diagnosing sleep disorders. We present in this work a model fusion approach to automate this task. ...
Humans Sleep Stages Polysomnography Computer Simulation Sleep Wake Disorders Deep Learning
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Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2019
BACKGROUND: Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure.
Sleep Stages Computer Simulation Electrocardiography Support Vector Machine Heart Rate Humans Algorithms Biomarkers
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