scholarly journals Real-time, automatic, open-source sleep stage classification system using single EEG for mice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Taro Tezuka ◽  
Deependra Kumar ◽  
Sima Singh ◽  
Iyo Koyanagi ◽  
Toshie Naoi ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Eye Movement ◽  
Classification System ◽  
Sleep Stage ◽  
Series Data ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Stage Classification ◽  
Sleep Stage Classification

AbstractWe developed a real-time sleep stage classification system with a convolutional neural network using only a one-channel electro-encephalogram source from mice and universally available features in any time-series data: raw signal, spectrum, and zeitgeber time. To accommodate historical information from each subject, we included a long short-term memory recurrent neural network in combination with the universal features. The resulting system (UTSN-L) achieved 90% overall accuracy and 81% multi-class Matthews Correlation Coefficient, with particularly high-quality judgements for rapid eye movement sleep (91% sensitivity and 98% specificity). This system can enable automatic real-time interventions during rapid eye movement sleep, which has been difficult due to its relatively low abundance and short duration. Further, it eliminates the need for ordinal pre-calibration, electromyogram recording, and manual classification and thus is scalable. The code is open-source with a graphical user interface and closed feedback loop capability, making it easily adaptable to a wide variety of end-user needs. By allowing large-scale, automatic, and real-time sleep stage-specific interventions, this system can aid further investigations of the functions of sleep and the development of new therapeutic strategies for sleep-related disorders.

scholarly journals A Sleep Stage Classification Algorithm of Wearable System Based on Multiscale Residual Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qinghua Zhong ◽  
Haibo Lei ◽  
Qianru Chen ◽  
Guofu Zhou
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sleep Disorder ◽  
Eye Movement ◽  
Cross Validation ◽  
Sleep Stage ◽  
Classification Algorithm ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification

Sleep disorder is a serious public health problem. Unobtrusive home sleep quality monitoring system can better open the way of sleep disorder-related diseases screening and health monitoring. In this work, a sleep stage classification algorithm based on multiscale residual convolutional neural network (MRCNN) was proposed to detect the characteristics of electroencephalogram (EEG) signals detected by wearable systems and classify sleep stages. EEG signals were analyzed in each epoch of every 30 seconds, and then 5-class sleep stage classification, wake (W), rapid eye movement sleep (REM), and nonrapid eye movement sleep (NREM) including N1, N2, and N3 stages was outputted. Good results (accuracy rate of 92.06% and 91.13%, Cohen’s kappa of 0.7360 and 0.7001) were achieved with 5-fold cross-validation and independent subject cross-validation, respectively, which performed on European Data Format (EDF) dataset containing 197 whole-night polysomnographic sleep recordings. Compared with several representative deep learning methods, this method can easily obtain sleep stage information from single-channel EEG signals without specialized feature extraction, which is closer to clinical application. Experiments based on CinC2018 dataset also proved that the method has a good performance on large dataset and can provide support for sleep disorder-related diseases screening and health surveillance based on automatic sleep staging.

A real-time rapid eye movement sleep analyzer equipped with a portable wide-band polygraphic recorder

2003 ◽  
Vol 1 (2) ◽  
pp. 175-177
Author(s):  
Mitsuaki YAMAMOTO ◽  
Norihiro KATAYAMA ◽  
Mitsuyuki NAKAO ◽  
Ryo KITAHARA ◽  
Takashi UENO ◽  
...  
Keyword(s):  
Real Time ◽  
Eye Movement ◽  
Wide Band ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

High-throughput visual assessment of sleep stages in mice using machine learning

SLEEP ◽  
2021 ◽  
Author(s):  
Brian Geuther ◽  
Mandy Chen ◽  
Raymond J Galante ◽  
Owen Han ◽  
Jie Lian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Movement ◽  
High Throughput ◽  
Data Augmentation ◽  
Sleep Stage ◽  
Visual Assessment ◽  
Video Data ◽  
Sleep Stages ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep

Abstract Study Objectives Sleep is an important biological process that is perturbed in numerous diseases, and assessment its substages currently requires implantation of electrodes to carry out electroencephalogram/electromyogram (EEG/EMG) analysis. Although accurate, this method comes at a high cost of invasive surgery and experts trained to score EEG/EMG data. Here, we leverage modern computer vision methods to directly classify sleep substages from video data. This bypasses the need for surgery and expert scoring, provides a path to high-throughput studies of sleep in mice. Methods We collected synchronized high-resolution video and EEG/EMG data in 16 male C57BL/6J mice. We extracted features from the video that are time and frequency-based and used the human expert-scored EEG/EMG data to train a visual classifier. We investigated several classifiers and data augmentation methods. Results Our visual sleep classifier proved to be highly accurate in classifying wake, non-rapid eye movement sleep (NREM), and rapid eye movement sleep (REM) states, and achieves an overall accuracy of 0.92 +/- 0.05 (mean +/- SD). We discover and genetically validate video features that correlate with breathing rates, and show low and high variability in NREM and REM sleep, respectively. Finally, we apply our methods to non-invasively detect that sleep stage disturbances induced by amphetamine administration. Conclusions We conclude that machine learning based visual classification of sleep is a viable alternative to EEG/EMG based scoring. Our results will enable non-invasive high-throughput sleep studies and will greatly reduce the barrier to screening mutant mice for abnormalities in sleep.

Influence of sleep on tensor palatini EMG and upper airway resistance in normal men

1991 ◽  
Vol 70 (6) ◽  
pp. 2574-2581 ◽  
Author(s):  
D. J. Tangel ◽  
W. S. Mezzanotte ◽  
D. P. White
Keyword(s):  
Eye Movement ◽  
Airway Resistance ◽  
Sleep Stage ◽  
Upper Airway ◽  
Sleep Stages ◽  
Normal Male ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Airway Narrowing ◽  
Upper Airway Resistance

We propose that a sleep-induced decrement in the activity of the tensor palatini (TP) muscle could induce airway narrowing in the area posterior to the soft palate and therefore lead to an increase in upper airway resistance in normal subjects. We investigated the TP to determine the influence of sleep on TP muscle activity and the relationship between changing TP activity and upper airway resistance over the entire night and during short sleep-awake transitions. Seven normal male subjects were studied on a single night with wire electrodes placed in both TP muscles. Sleep stage, inspiratory airflow, transpalatal pressure, and TP moving time average electromyogram (EMG) were continuously recorded. In addition, in two of the seven subjects the activity (EMG) of both the TP and the genioglossus muscle simultaneously was recorded throughout the night. Upper airway resistance increased progressively from wakefulness through the various non-rapid-eye-movement sleep stages, as has been previously described. The TP EMG did not commonly demonstrate phasic activity during wakefulness or sleep. However, the tonic EMG decreased progressively and significantly (P less than 0.05) from wakefulness through the non-rapid-eye-movement sleep stages [awake, 4.6 +/- 0.3 (SE) arbitrary units; stage 1, 2.6 +/- 0.3; stage 2, 1.7 +/- 0.5; stage 3/4, 1.5 +/- 0.8]. The mean correlation coefficient between TP EMG and upper airway resistance across all sleep states was (-0.46). This mean correlation improved over discrete sleep-awake transitions (-0.76). No sleep-induced decrement in the genioglossus activity was observed in the two subjects studied.(ABSTRACT TRUNCATED AT 250 WORDS)

scholarly journals Dream Recall upon Awakening from Non-Rapid Eye Movement Sleep in Older Adults: Electrophysiological Pattern and Qualitative Features

2020 ◽  
Vol 10 (6) ◽  
pp. 343 ◽  
Author(s):  
Serena Scarpelli ◽  
Aurora D’Atri ◽  
Chiara Bartolacci ◽  
Maurizio Gorgoni ◽  
Anastasia Mangiaruga ◽  
...  
Keyword(s):  
Eye Movement ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Dream Recall ◽  
Cortical Arousal ◽  
Beta Power ◽  
Dream Report

Several findings support the activation hypothesis, positing that cortical arousal promotes dream recall (DR). However, most studies have been carried out on young participants, while the electrophysiological (EEG) correlates of DR in older people are still mostly unknown. We aimed to test the activation hypothesis on 20 elders, focusing on the Non-Rapid Eye Movement (NREM) sleep stage. All the subjects underwent polysomnography, and a dream report was collected upon their awakening from NREM sleep. Nine subjects were recallers (RECs) and 11 were non-RECs (NRECs). The delta and beta EEG activity of the last 5 min and the total NREM sleep was calculated by Fast Fourier Transform. Statistical comparisons (RECs vs. NRECs) revealed no differences in the last 5 min of sleep. Significant differences were found in the total NREM sleep: the RECs showed lower delta power over the parietal areas than the NRECs. Consistently, statistical comparisons on the activation index (delta/beta power) revealed that RECs showed a higher level of arousal in the fronto-temporal and parieto-occipital regions than NRECs. Both visual vividness and dream length are positively related to the level of activation. Overall, our results are consistent with the view that dreaming and the storage of oneiric contents depend on the level of arousal during sleep, highlighting a crucial role of the temporo-parietal-occipital zone.

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network

2018 ◽  
Vol 63 (2) ◽  
pp. 177-190 ◽  
Author(s):  
Junming Zhang ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Channel ◽  
Sleep Stage ◽  
Experimental Results ◽  
Sleep Stages ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Complex Valued ◽  
Learned Features

AbstractMany systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

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