Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography

2016 ◽  
Vol 25 (6) ◽  
pp. 636-645 ◽  
Author(s):  
Sami Myllymaa ◽  
Anu Muraja-Murro ◽  
Susanna Westeren-Punnonen ◽  
Taina Hukkanen ◽  
Reijo Lappalainen ◽  
...  
Keyword(s):  
Sleep Staging ◽  
Emg Electrodes

scholarly journals Accurate Deep Learning-Based Sleep Staging in a Clinical Population with Suspected Obstructive Sleep Apnea

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Henri Korkalainen ◽  
Timo Leppanen ◽  
Juhani Aakko ◽  
Sami Nikkonen ◽  
Samu Kainulainen ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Clinical Population ◽  
Sleep Staging ◽  
Obstructive Sleep

scholarly journals A Multi-Class Automatic Sleep Staging Method Based on Photoplethysmography Signals

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 116
Author(s):  
Xiangfa Zhao ◽  
Guobing Sun
Keyword(s):  
Time Domain ◽  
Single Channel ◽  
Kappa Statistic ◽  
Gradient Boosting ◽  
Sleep Staging ◽  
Challenging Problem ◽  
Sleep State ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
The Time Domain

Automatic sleep staging with only one channel is a challenging problem in sleep-related research. In this paper, a simple and efficient method named PPG-based multi-class automatic sleep staging (PMSS) is proposed using only a photoplethysmography (PPG) signal. Single-channel PPG data were obtained from four categories of subjects in the CAP sleep database. After the preprocessing of PPG data, feature extraction was performed from the time domain, frequency domain, and nonlinear domain, and a total of 21 features were extracted. Finally, the Light Gradient Boosting Machine (LightGBM) classifier was used for multi-class sleep staging. The accuracy of the multi-class automatic sleep staging was over 70%, and the Cohen’s kappa statistic k was over 0.6. This also showed that the PMSS method can also be applied to stage the sleep state for patients with sleep disorders.

109 Chemogenetic silencing of corticotropin releasing factor neurons in the paraventricular nucleus: the effect on post-stress sleep

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A45-A45
Author(s):  
Irma Gvilia ◽  
Sunil Kumar ◽  
Dennis McGinty ◽  
Ronald Szymusiak
Keyword(s):  
Paraventricular Nucleus ◽  
Nrem Sleep ◽  
Corticotropin Releasing Factor ◽  
Male Rats ◽  
Male Rat ◽  
Sleep Onset Latency ◽  
Post Surgery ◽  
Crf Neurons ◽  
Emg Electrodes ◽  
Post Stress

Abstract Introduction We have previously shown that pharmacological elevation of corticotropin releasing factor (CRF) signaling in the brain results in exacerbation of sleep disturbances evoked by the exposure of rats to an acute stressor, the dirty cage of a male rat. In the present study we (1) assessed wake-sleep behavior of mice after the exposure to the dirty cage stress paradigm, and (2) examined the effect of chemogenetic silencing of CRF neurons in the hypothalamic paraventricular nucleus (PVN) on sleep occurring following the exposure to this stressor. Methods First, a group of mice (n=12) was implanted with EEG/EMG electrodes. In two weeks, post-surgery, six mice were transferred to dirty cages of male rats and recorded for 24 hours. Control mice were transferred to clean cages. In the second study, a group of CRF-ires-cre mice (n=8) received bilateral injections of AAV-hSyn-DIO-hM4Di-mCherry targeting the PVN. The other group of CRF-ires-cre mice (n=8) was injected AAV-hSyn-DIO-mCherry (control vector). All mice were implanted with EEG/EMG electrodes. Dirty cage experiments were started following a 4-week postsurgical period to allow gene recombination and expression. Mice were subjected to intraperitoneal (IP) administration of clozapine-n-oxide (CNO; 3 mg/kg) at ZT1, placed into dirty cages, and recorded for post-stress sleep. Results: Results In mice expressing hM4Di inhibitory DREADDs (designer receptors activated by designer drugs) versus mice injected with control AAV, IP CNO (3 mg/kg) resulted in a significant decrease of post-stress sleep onset latency, decrease of time spent in wakefulness (first hour, 74±5.3 vs. 89±11.0, second hour, 37.2±10.3% vs. 81.3±9.3%; third hour, 40.1±3.3% vs. 47.1±14.3%; fourth hour, 44.4±6.0 vs. 55.5±9.9), and increase in non-rapid eye movement (NREM) sleep time (26.0±5.4% vs. 11.0±11.1%; 62.8%±9.8 vs. 18.7 ± 9.6%; 59.9±3.2% vs. 52.9±14.5%; 55.6±6.2 vs. 44.5±10.0). The hM4Di expressing mice exhibited longer episodes of NREM sleep, compared to mice injected with control AAV (first hour, 133.3±80.1sec vs. 21±1.7sec; second hour, 43256±83.4sec vs. 73.5±44.1sec; third hour, 459.2±139.8sec vs. 139±80.6sec; fourth hour, 233.1±82.6sec vs. 190±72.3sec). Conclusion Chemogenetic silencing of CRF neurons in the PVN attenuates acute stress-induced sleep disturbance in mice. Support (if any) Supported by Department of Veterans Affairs Merit Review Grant # BX00155605 and SRNSF (Georgia) grant FR-18-12533

scholarly journals A Systematic Review of Sensing Technologies for Wearable Sleep Staging

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1562
Author(s):  
Syed Anas Imtiaz
Keyword(s):  
Systematic Review ◽  
Classification Accuracy ◽  
Sleep Stages ◽  
Sleep Staging ◽  
Regulatory Requirements ◽  
Wearable System ◽  
Wearable Systems

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages.

250 AI-Supported Sleep Staging from Activity and Heart Rate

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Class Imbalance ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Temporal Correlations ◽  
Stage Classification ◽  
Small Form Factor

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

Printed Multi-EMG Electrodes on the 3D Surface of an Orthosis for Rehabilitation: a Feasibility Study

2021 ◽  
pp. 1-1
Author(s):  
Edoardo Cantu ◽  
Tiziano Fapanni ◽  
Giada Giorgi ◽  
Claudio Narduzzi ◽  
Emilio Sardini ◽  
...  
Keyword(s):  
Feasibility Study ◽  
3D Surface ◽  
Emg Electrodes

scholarly journals 0004 TS-142: A Novel and Potent Dual Orexin Receptor Antagonist with Sleep-Promoting Effects in Rats

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A2-A2
Author(s):  
D Kambe ◽  
H Hikichi ◽  
Y Tokumaru ◽  
M Ohmichi ◽  
Y Konno ◽  
...  
Keyword(s):  
Receptor Antagonist ◽  
Half Life ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Orexin A ◽  
Pharmacokinetic Profiles ◽  
Orexin Receptors ◽  
Elimination Half ◽  
Emg Electrodes

Abstract Introduction The orexin system plays a pivotal role in regulating sleep and wakefulness, thus, orexin receptors (OX1 and OX2 receptors) have gained much attention as promising therapeutic targets for the treatment of insomnia. We synthesized a novel and potent dual orexin receptor antagonist (DORA), ORN0829 (investigation code name as TS-142), which was designed to have short-acting effects. Here we report pharmacological and pharmacokinetic profiles of ORN0829 in rats. Methods The antagonistic activities of ORN0829 were assessed using calcium mobilization assays. Ala-orexin A-induced [Ca2+]i response was measured with CHO-K1 cells stably expressing human/rat orexin receptor. Rats implanted the EEG/EMG electrodes were orally administrated ORN0829 at doses of 1, 3 or 10 mg/kg at the dark onset and sleep-wake stages were inspected visually. In addition, pharmacokinetic profiles of ORN0829 were investigated in rats. Results ORN0829 inhibited Ala-orexin A-increased [Ca2+]i response with a Kb of 0.67/0.44 nmol/L (for human/rat OX1 receptor), and with a Kb of 0.84/0.80 nmol/L (for human/rat OX2 receptor), respectively, indicating that ORN0829 is a potent DORA with no species differences. ORN0829 dose-dependently increased total sleep time and reduced sleep onset latency at doses of 1, 3 and 10 mg/kg. Importantly, the ORN0829 levels in plasma and cerebrospinal fluid rapidly reached a maximum concentration, and decreased with an elimination half-life of less than 1 h. Conclusion The present study indicates that ORN0829 is a novel and potent DORA with sleep-promoting effects, and that it exhibits ideal pharmacokinetic profiles (rapid absorption and short half-life) in rats. A phase 2a study of TS-142 using patients with insomnia has been completed, which is presented in a separate poster. Support Taisho Pharmaceutical. Co., Ltd.

Detection of the Sleep Stages Throughout Non-Obtrusive Measures of Inter-Beat Fluctuations and Motion: Night and Day Sleep of Female Shift Workers

2017 ◽  
Vol 16 (04) ◽  
pp. 1750033 ◽  
Author(s):  
Martin O. Mendez ◽  
Elvia R. Palacios-Hernandez ◽  
Alfonso Alba ◽  
Juha M. Kortelainen ◽  
Mirja L. Tenhunen ◽  
...  
Keyword(s):  
Eye Movement ◽  
Performance Measures ◽  
Night Shift ◽  
Sleep Stages ◽  
Support Vector ◽  
Rapid Eye Movement ◽  
Sleep Staging ◽  
Kappa Index ◽  
Extensive Evaluation ◽  
Night And Day

Automatic sleep staging based on inter-beat fluctuations and motion signals recorded through a pressure bed sensor during sleep is presented. The analysis of the sleep was based on the three major divisions of the sleep time: Wake, non-rapid eye movement (nREM) and rapid eye movement (REM) sleep stages. Twelve sleep recordings, from six females working alternate shift, with their respective annotations were used in the study. Six recordings were acquired during the night and six during the day after a night shift. A Time-Variant Autoregressive Model was used to extract features from inter-beat fluctuations which later were fed to a Support Vector Machine classifier. Accuracy, Kappa index, and percentage in wake, REM and nREM were used as performance measures. Comparison between the automatic sleep staging detection and the standard clinical annotations, shows mean values of [Formula: see text]% for accuracy [Formula: see text] for kappa index, and mean errors of 5% for sleep stages. The performance measures were similar for night and day sleep recordings. In this sample of recordings, the results suggest that inter-beat fluctuations and motions acquired in non-obtrusive way carried valuable information related to the sleep macrostructure and could be used to support to the experts in extensive evaluation and monitoring of sleep.

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