AIMC Topic: Sleep Stages

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A Model Visualization-based Approach for Insight into Waveforms and Spectra Learned by CNNs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2022
Recent years have shown a growth in the application of deep learning architectures such as convolutional neural networks (CNNs), to electrophysiology analysis. However, using neural networks with raw time-series data makes explainability a significan...
Neural Networks, Computer Sleep Sleep Stages
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A deep learning algorithm based on 1D CNN-LSTM for automatic sleep staging.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2022
BACKGROUND: Sleep staging is an important part of sleep research. Traditional automatic sleep staging based on machine learning requires extensive feature extraction and selection.
Algorithms Deep Learning Electroencephalography Humans Neural Networks, Computer Sleep Sleep Stages
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[The accuracy and influencing factors of sleep staging based on single-channel EEG via a deep neural network].

Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Dec 7, 2021
To investigate theaccuracy of artificial intelligence sleep staging model in patients with habitual snoring and obstructive sleep apnea hypopnea syndrome (OSAHS) based on single-channel EEG collected from different locations of the head. The clinic...
Humans Neural Networks, Computer Artificial Intelligence Male Middle Aged Young Adult Sleep Electroencephalography Adult Sleep Stages Retrospective Studies Female
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Machine Learning-based Sleep Staging in Patients with Sleep Apnea Using a Single Mandibular Movement Signal.

American journal of respiratory and critical care medicine Nov 15, 2021
Mandible Sleep Apnea Syndromes Pattern Recognition, Automated Male Middle Aged Polysomnography Sleep Stages Algorithms Humans Machine Learning Prospective Studies Adult Sensitivity and Specificity Female
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LightSleepNet: A Lightweight Deep Model for Rapid Sleep Stage Classification with Spectrograms.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Nov 1, 2021
Deep learning has achieved unprecedented success in sleep stage classification tasks, which starts to pave the way for potential real-world applications. However, due to its enormous size, deployment of deep neural networks is hindered by high cost a...
Sleep Sleep Stages Deep Learning Electroencephalography Neural Networks, Computer
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Interrater sleep stage scoring reliability between manual scoring from two European sleep centers and automatic scoring performed by the artificial intelligence-based Stanford-STAGES algorithm.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Jun 1, 2021
STUDY OBJECTIVES: The objective of this study was to evaluate interrater reliability between manual sleep stage scoring performed in 2 European sleep centers and automatic sleep stage scoring performed by the previously validated artificial intellige...
Humans Observer Variation Sleep Stages Reproducibility of Results Artificial Intelligence Electroencephalography Algorithms Sleep
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Large-scale assessment of consistency in sleep stage scoring rules among multiple sleep centers using an interpretable machine learning algorithm.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Feb 1, 2021
STUDY OBJECTIVES: Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multipl...
Reproducibility of Results Taiwan Sleep Stages Machine Learning Humans Sleep Algorithms Artificial Intelligence
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Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning.

Sleep Nov 12, 2020
STUDY OBJECTIVES: Develop a high-performing, automated sleep scoring algorithm that can be applied to long-term scalp electroencephalography (EEG) recordings.
Sleep Stages Deep Learning Sleep Humans Electroencephalography Scalp
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Deep learning enables sleep staging from photoplethysmogram for patients with suspected sleep apnea.

Sleep Nov 12, 2020
STUDY OBJECTIVES: Accurate identification of sleep stages is essential in the diagnosis of sleep disorders (e.g. obstructive sleep apnea [OSA]) but relies on labor-intensive electroencephalogram (EEG)-based manual scoring. Furthermore, long-term asse...
Humans Reproducibility of Results Deep Learning Sleep Sleep Apnea Syndromes Sleep Stages Photoplethysmography
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Beyond K-complex binary scoring during sleep: probabilistic classification using deep learning.

Sleep Oct 13, 2020
STUDY OBJECTIVES: K-complexes (KCs) are a recognized electroencephalography marker of sensory processing and a defining feature of sleep stage 2. KC frequency and morphology may also be reflective of sleep quality, aging, and a range of sleep and sen...
Polysomnography Electroencephalography Humans Sleep Sleep Stages Deep Learning
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