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Sleep, REM

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Exploring the clinical features of narcolepsy type 1 versus narcolepsy type 2 from European Narcolepsy Network database with machine learning.

Scientific reports
Narcolepsy is a rare life-long disease that exists in two forms, narcolepsy type-1 (NT1) or type-2 (NT2), but only NT1 is accepted as clearly defined entity. Both types of narcolepsies belong to the group of central hypersomnias (CH), a spectrum of p...

Automated scoring of pre-REM sleep in mice with deep learning.

Scientific reports
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accurac...

[Discrimination of Chin Electromyography in REM Sleep Behavior Disorder Using Deep Learning].

Nihon eiseigaku zasshi. Japanese journal of hygiene
OBJECTIVE: The confirmation of abnormal behavior during video monitoring in polysomnography (PSG) and the frequency of rapid eye movement (REM) sleep without atonia (RWA) during REM sleep based on physiological indicators are essential diagnostic cri...

Convolutional neural network is a good technique for sleep staging based on HRV: A comparative analysis.

Neuroscience letters
The fluctuation of heart rate is regulated by autonomic nervous system. In human sleep, the autonomic nervous system plays a leading role. Therefore, we can use heart-rate variability (HRV) to stage the sleep process. Based on two independent public ...

Non-Contact REM Sleep Estimation Correction by Time-Series Confidence of Predictions: From Binary to Continuous Prediction in Machine Learning for Biological Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its "correction" method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of th...

[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
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.

Rapid eye movement sleep loss associated cytomorphometric changes and neurodegeneration.

Sleep medicine
Rapid eye movement sleep (REMS) is essential for leading normal healthy living at least in higher-order mammals, including humans. In this review, we briefly survey the available literature for evidence linking cytomorphometric changes in the brain d...

Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of var...

Sleep structure discriminates patients with isolated REM sleep behavior disorder: a deep learning approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Rapid eye movement (REM) sleep behavior disorder (RBD) is a disorder characterized by increased muscle tone and dream-enactment behaviors in REM sleep. In its isolated form (iRBD), it is a prodromal stage of neurodegenerative diseases. Currently, dia...

Unlocking Dreams and Dreamless Sleep: Machine Learning Classification With Optimal EEG Channels.

BioMed research international
Research suggests that dreams play a role in the regulation of emotional processing and memory consolidation; electroencephalography (EEG) is useful for studying them, but manual annotation is time-consuming and prone to bias. This study was aimed at...