AIMC Topic: Sleep Stages

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Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis and Beyond.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present nov...
Humans Neural Networks, Computer Sleep Polysomnography Sleep Stages Machine Learning Algorithms
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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2024
Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep st...
Humans Neural Networks, Computer Electrooculography Sleep Stages Young Adult Adult Algorithms Female Signal Processing, Computer-Assisted Electroencephalography Male Polysomnography Deep Learning
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Are pictures worth a thousand sleep signals?

Sleep Dec 11, 2023
Polysomnography Sleep Stages Deep Learning Sleep
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Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification.

Sleep Dec 11, 2023
STUDY OBJECTIVES: Polysomnography (PSG) scoring is labor-intensive, subjective, and often ambiguous. Recently several deep learning (DL) models for automated sleep scoring have been developed, they are tied to a fixed amount of input channels and res...
Algorithms Deep Learning Sleep Stages Sleep Polysomnography
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A convolutional neural network-based decision support system for neonatal quiet sleep detection.

Mathematical biosciences and engineering : MBE Aug 29, 2023
Sleep plays an important role in neonatal brain and physical development, making its detection and characterization important for assessing early-stage development. In this study, we propose an automatic and computationally efficient algorithm to det...
Sleep Sleep Stages Child Electroencephalography Infant, Newborn China Neural Networks, Computer Algorithms Humans
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Identification of Sleep Patterns via Clustering of Hypnodensities.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Sleep patterns vary widely between individuals. We explore methods for identifying populations exhibiting similar sleep patterns in an automated fashion using polysomnography data. Our novel approach applies unsupervised machine learning algorithms t...
Cluster Analysis Polysomnography Neural Networks, Computer Sleep Stages Humans Algorithms
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A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate d...
Oximetry Humans Sleep Stages Sleep Apnea Syndromes Neural Networks, Computer Sleep Apnea, Obstructive Deep Learning Child
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[Identifying Depressive Disorder With Sleep Electroencephalogram Data: A Study Based on Deep Learning].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition Mar 1, 2023
OBJECTIVE: To explore the effectiveness of using deep learning network combined Vision Transformer (ViT) and Transformer to identify patients with depressive disorder on the basis of their sleep electroencephalogram (EEG) signals.
Electroencephalography Humans Sleep, REM Depressive Disorder Deep Learning Sleep Stages
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Scoring sleep with artificial intelligence enables quantification of sleep stage ambiguity: hypnodensity based on multiple expert scorers and auto-scoring.

Sleep Feb 8, 2023
STUDY OBJECTIVES: To quantify the amount of sleep stage ambiguity across expert scorers and to validate a new auto-scoring platform against sleep staging performed by multiple scorers.
Observer Variation Humans Sleep Stages Artificial Intelligence Reproducibility of Results Sleep
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A Developed LSTM-Ladder-Network-Based Model for Sleep Stage Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2023
Sleep staging is crucial for diagnosing sleep-related disorders. The heavy and time-consuming task of manual staging can be released by automatic techniques. However, the automatic staging model would have a relatively poor performance when working o...
Sleep Sleep Stages Humans Neural Networks, Computer Learning Electroencephalography
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