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PloS One 2019Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes...
Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the baby's brain signal complexity (and spectral power) revealed developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM ("quiet") and REM ("active sleep") states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques.
Topics: Brain; Electroencephalography; Female; Humans; Infant, Newborn; Machine Learning; Male; Polysomnography; Sleep; Sleep Stages; Sleep, REM; Wakefulness
PubMed: 31661522
DOI: 10.1371/journal.pone.0224521 -
Lin Chuang Er Bi Yan Hou Tou Jing Wai... Jun 2021To explore the differences in cognitive function between patients with severe OSA and non-moderate OSA. The MoCA scale was used to evaluate the overall cognitive...
To explore the differences in cognitive function between patients with severe OSA and non-moderate OSA. The MoCA scale was used to evaluate the overall cognitive function and sub-items in 196 subjects who received polysomnography; and the SDMT and TMT-A scales were used to evaluate the performance in test of attention and information processing speed in 161 patients. The clinical information, physical examination data and related polysomnography data were collected. According to AHI, subjects were divided into two groups: severe OSA and non-to-moderate OSA. Before and after correction of confounding factors, the differences in cognitive scale evaluation indicators were compared between the two groups. We used linear regression analysis to clarify the independent influencing factors of cognitive functions, and to determine whether severe OSA is independently related to cognitive abilities. After correcting for multiple factors, the delayed recall score and total score of the MoCA scale and the correct number of SDMT in the severe OSA group were significantly lower than those in the non-to-moderate OSA group(<0.05). Linear regression analysis showed that severe OSA was independently negatively correlated with the delayed recall score, total score and SDMT correct number in the MoCA scale(<0.05). Compared with non-to-moderate OSA, subjects with severe OSA have significant decline in overall cognition, delayed recall, attention and processing speed. Severe OSA may be an independent influencing factor of overall cognition, delayed recall, attention and processing speed.
Topics: Attention; Cognition; Humans; Polysomnography; Sleep Apnea, Obstructive
PubMed: 34304509
DOI: 10.13201/j.issn.2096-7993.2021.06.006 -
Annals of the American Thoracic Society Apr 2021There are at least four key pathophysiological endotypes that contribute to obstructive sleep apnea (OSA) pathophysiology. These include ) upper-airway collapsibility...
A Novel Model to Estimate Key Obstructive Sleep Apnea Endotypes from Standard Polysomnography and Clinical Data and Their Contribution to Obstructive Sleep Apnea Severity.
There are at least four key pathophysiological endotypes that contribute to obstructive sleep apnea (OSA) pathophysiology. These include ) upper-airway collapsibility (Pcrit); ) arousal threshold; ) loop gain; and ) pharyngeal muscle responsiveness. However, an easily interpretable model to examine the different ways and the extent to which these OSA endotypes contribute to conventional polysomnography-defined OSA severity (i.e., the apnea-hypopnea index) has not been investigated. In addition, clinically deployable approaches to estimate OSA endotypes to advance knowledge on OSA pathogenesis and targeted therapy at scale are not currently available. Develop an interpretable data-driven model to ) determine the different ways and the extent to which the four key OSA endotypes contribute to polysomnography-defined OSA severity and ) gain insight into how standard polysomnographic and clinical variables contribute to OSA endotypes and whether they can be used to predict OSA endotypes. Age, body mass index, and eight polysomnography parameters from a standard diagnostic study were collected. OSA endotypes were also quantified in 52 participants (43 participants with OSA and nine control subjects) using gold-standard physiologic methodology on a separate night. Unsupervised multivariate principal component analyses and data-driven supervised machine learning (decision tree learner) were used to develop a predictive algorithm to address the study objectives. Maximum predictive performance accuracy of the trained model to identify standard polysomnography-defined OSA severity levels (no OSA, mild to moderate, or severe) using the using the four OSA endotypes was approximately twice that of chance. Similarly, performance accuracy to predict OSA endotype categories ("good," "moderate," or "bad") from standard polysomnographic and clinical variables was approximately twice that of chance for Pcrit and slightly lower for arousal threshold. This novel approach provides new insights into the different ways in which OSA endotypes can contribute to polysomnography-defined OSA severity. Although further validation work is required, these findings also highlight the potential for routine sleep study and clinical data to estimate at least two of the key OSA endotypes using data-driven predictive analysis methodology as part of a clinical decision support system to inform scalable research studies to advance OSA pathophysiology and targeted therapy for OSA.
Topics: Arousal; Body Mass Index; Humans; Polysomnography; Sleep Apnea, Obstructive
PubMed: 33064953
DOI: 10.1513/AnnalsATS.202001-064OC -
Sleep Health Oct 2022To characterize and evaluate the estimation of oxygen saturation measured by a wrist-worn reflectance pulse oximeter during sleep.
OBJECTIVES
To characterize and evaluate the estimation of oxygen saturation measured by a wrist-worn reflectance pulse oximeter during sleep.
METHODS
Ninety-seven adults with sleep disturbances were enrolled. Oxygen saturation was simultaneously measured using a reflectance pulse oximeter (Galaxy Watch 4 [GW4], Samsung, South Korea) and a transmittance pulse oximeter (polysomnography) as a reference. The performance of the device was evaluated using the root mean squared error (RMSE) and coverage rate. Additionally, GW4-derived oxygen desaturation index (ODI) was compared with the apnea-hypopnea index (AHI) derived from polysomnography.
RESULTS
The GW4 had an overall RMSE of 2.3% and negligible bias of -0.2%. A Bland-Altman density plot showed good agreement between the GW4 and the reference pulse oximeter. RMSEs were 1.65 ± 0.57%, 1.76 ± 0.65%, 1.93 ± 0.54%, and 2.93 ± 1.71% for normal (n = 18), mild (n = 21), moderate (n = 23), and severe obstructive sleep apnea (n = 35), respectively. The data rejection rate was 26.5%, which was caused by fluctuations in contact pressure and the discarding of data less than 70% of saturation. A GW4-ODI ≥5/h had the highest ability to predict AHI ≥15/h with sensitivity, specificity, accuracy, and area under the curve of 89.7%, 64.1%, 79.4%, and 0.908, respectively.
CONCLUSIONS
This study evaluated the estimation of oxygen saturation by the GW4 during sleep. This device complies with both Food and Drug Administration and International Organization for Standardization standards. Further improvements in the algorithms of wearable devices are required to obtain more accurate and reliable information about oxygen saturation measurements.
Topics: United States; Adult; Humans; Wrist; Oximetry; Polysomnography; Sleep; Oxygen
PubMed: 35817700
DOI: 10.1016/j.sleh.2022.04.003 -
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi =... Dec 2021Sleep is a complex physiological process of great significance to physical and mental health, and its research scope involves multiple disciplines. At present, the... (Review)
Review
Sleep is a complex physiological process of great significance to physical and mental health, and its research scope involves multiple disciplines. At present, the quantitative analysis of sleep mainly relies on the "gold standard" of polysomnography (PSG). However, PSG has great interference to the human body and cannot reflect the hemodynamic status of the brain. Functional near infrared spectroscopy (fNIRS) is used in sleep research, which can not only meet the demand of low interference to human body, but also reflect the hemodynamics of brain. Therefore, this paper has collected and sorted out the related literatures about fNIRS used in sleep research, concluding sleep staging research, clinical sleep monitoring research, fatigue detection research, etc. This paper provides a theoretical reference for scholars who will use fNIRS for fatigue and sleep related research in the future. Moreover, this article concludes the limitation of existing studies and points out the possible development direction of fNIRS for sleep research, in the hope of providing reference for the study of sleep and cerebral hemodynamics.
Topics: Brain; Hemodynamics; Humans; Polysomnography; Sleep; Spectroscopy, Near-Infrared
PubMed: 34970905
DOI: 10.7507/1001-5515.202102003 -
Sensors (Basel, Switzerland) Jul 2022Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task,...
INTRODUCTION
Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals.
METHODS
Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner.
RESULTS
The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.
Topics: Algorithms; Biosensing Techniques; Humans; Machine Learning; Polysomnography; Sensitivity and Specificity; Sleep; Sleep Apnea Syndromes; Sleep Apnea, Obstructive; Support Vector Machine
PubMed: 35898064
DOI: 10.3390/s22155560 -
American Journal of Respiratory and... Mar 2021
Topics: Child; Humans; Polysomnography; Problem Behavior; Sleep Apnea Syndromes
PubMed: 33352057
DOI: 10.1164/rccm.202011-4291ED -
Sensors (Basel, Switzerland) May 2023Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of...
Sleep stage detection from polysomnography (PSG) recordings is a widely used method of monitoring sleep quality. Despite significant progress in the development of machine-learning (ML)-based and deep-learning (DL)-based automatic sleep stage detection schemes focusing on single-channel PSG data, such as single-channel electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG), developing a standard model is still an active subject of research. Often, the use of a single source of information suffers from data inefficiency and data-skewed problems. Instead, a multi-channel input-based classifier can mitigate the aforementioned challenges and achieve better performance. However, it requires extensive computational resources to train the model, and, hence, a tradeoff between performance and computational resources cannot be ignored. In this article, we aim to introduce a multi-channel, more specifically a four-channel, convolutional bidirectional long short-term memory (Bi-LSTM) network that can effectively exploit spatiotemporal features of data collected from multiple channels of the PSG recording (e.g., EEG Fpz-Cz, EEG Pz-Oz, EOG, and EMG) for automatic sleep stage detection. First, a dual-channel convolutional Bi-LSTM network module has been designed and pre-trained utilizing data from every two distinct channels of the PSG recording. Subsequently, we have leveraged the concept of transfer learning circuitously and have fused two dual-channel convolutional Bi-LSTM network modules to detect sleep stages. In the dual-channel convolutional Bi-LSTM module, a two-layer convolutional neural network has been utilized to extract spatial features from two channels of the PSG recordings. These extracted spatial features are subsequently coupled and given as input at every level of the Bi-LSTM network to extract and learn rich temporal correlated features. Both Sleep EDF-20 and Sleep EDF-78 (expanded version of Sleep EDF-20) datasets are used in this study to evaluate the result. The model that includes an EEG Fpz-Cz + EOG module and an EEG Fpz-Cz + EMG module can classify sleep stage with the highest value of accuracy (), Kappa (), and (e.g., 91.44%, 0.89, and 88.69%, respectively) on the Sleep EDF-20 dataset. On the other hand, the model consisting of an EEG Fpz-Cz + EMG module and an EEG Pz-Oz + EOG module shows the best performance (e.g., the value of , , and are 90.21%, 0.86, and 87.02%, respectively) compared to other combinations for the Sleep EDF-78 dataset. In addition, a comparative study with respect to other existing literature has been provided and discussed in order to exhibit the efficacy of our proposed model.
Topics: Sleep Stages; Sleep; Polysomnography; Electroencephalography; Electromyography
PubMed: 37430865
DOI: 10.3390/s23104950 -
Journal of Clinical Sleep Medicine :... Oct 2019Palen BN, He K, Redinger J, Parsons EC. A change of heart. 2019;15(10):1543–1545.
Palen BN, He K, Redinger J, Parsons EC. A change of heart. 2019;15(10):1543–1545.
Topics: Aged; Continuous Positive Airway Pressure; Heart Failure; Heart Ventricles; Heart-Assist Devices; Humans; Male; Polysomnography; Sleep Apnea Syndromes
PubMed: 31596222
DOI: 10.5664/jcsm.7998 -
Sleep Medicine May 2021Sleep quality typically decreases after menopause, but the underlying mechanisms are poorly understood. Concentrations of melatonin are lower and its secretion profiles...
BACKGROUND
Sleep quality typically decreases after menopause, but the underlying mechanisms are poorly understood. Concentrations of melatonin are lower and its secretion profiles different before and after menopause. However, whether and how melatonin and sleep architecture are associated in women of different reproductive states have not been examined to date.
METHODS
Overnight serum melatonin samples were taken from 17 perimenopausal and 18 postmenopausal healthy women. Sleep quality was measured with all-night polysomnography recordings.
RESULTS
Melatonin concentrations tended to be the lowest during NREM sleep, and were associated with higher odds of transitions from wake to NREM sleep. The curves of predicted overnight melatonin values from linear mixed models varied according to sleep phases (NREM, REM, Wake) in perimenopausal, but not in postmenopausal women. In perimenopause higher melatonin area under curve (AUC) correlated with higher slow-wave activity (p = 0.043), and higher minimum concentrations with shorter slow-wave sleep (SWS) latency (p = 0.029). In postmenopause higher mean and maximum melatonin concentrations and AUC correlated with lower SWS percentage (p = 0.044, p = 0.029, p = 0.032), and higher mean (p = 0.032), maximum (p = 0.032) and minimum (p = 0.037) concentrations with more awakenings from REM sleep. In the age- and BMI- adjusted regression models, the association between higher maximum (p = 0.046) melatonin concentration and lower SWS percentage remained.
CONCLUSIONS
The relationship between melatonin and sleep architecture differed in perimenopausal and postmenopausal women. After menopause, high melatonin concentrations were associated with worse sleep. Whether these different patterns are related to aging of the reproductive system, and to decrease in menopausal sleep quality, remains to be elucidated.
Topics: Female; Humans; Melatonin; Perimenopause; Polysomnography; Postmenopause; Sleep
PubMed: 33639482
DOI: 10.1016/j.sleep.2021.02.011