Sleep staging using nocturnal sound analysis.
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ABSTRACT:
SUBMITTER: Dafna E
PROVIDER: S-EPMC6128888 | biostudies-other | 2018 Sep
REPOSITORIES: biostudies-other
Publications
Sleep staging using nocturnal sound analysis.
Dafna Eliran E Tarasiuk Ariel A Zigel Yaniv Y
Scientific reports 20180907 1
Sleep staging is essential for evaluating sleep and its disorders. Most sleep studies today incorporate contact sensors that may interfere with natural sleep and may bias results. Moreover, the availability of sleep studies is limited, and many people with sleep disorders remain undiagnosed. Here, we present a pioneering approach for rapid eye movement (REM), non-REM, and wake staging (macro-sleep stages, MSS) estimation based on sleep sounds analysis. Our working hypothesis is that the properti ...[more]
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Project description:STUDY OBJECTIVES:Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries information about apnea/hypopnea and sleep/wake status. We hypothesized that image analysis of all-night TS recordings by a deep neural network (DNN) would be capable of detecting breathing events and classifying sleep/wake status. The aim of this study is to develop a DNN-based system for sleep apnea testing and validate it using a large sampling of polysomnography (PSG) data. METHODS:PSG examinations for the evaluation of sleep-disordered breathing (SDB) were performed for 1,852 patients: 1,548 PSG records were used to develop the system, and the remaining 304 records were used for validation. TS spectrogram images were obtained every 60 seconds and labeled with the PSG scoring results (breathing event and sleep/wake status), then introduced to DNN learning. Two different DNNs were trained for breathing status and sleep/wake status, respectively. RESULTS:A DNN with convolutional layers showed the best performance for discriminating breathing status. The same DNN structure was trained for sleep/wake discrimination. In the validation study, the DNN analysis was capable of discriminating the sleep/wake status with reasonable accuracy. The diagnostic sensitivity, specificity, and area under the receiver operating characteristic curves for diagnosis of SDB with apnea-hypopnea index of > 5, 15, and 30 were 0.98, 0.76, and 0.99; 0.97, 0.90, and 0.99; and 0.92, 0.94, and 0.98, respectively. CONCLUSIONS:The developed system using the TS DNN analysis has a good performance for SDB testing. CITATION:Nakano H, Furukawa T, Tanigawa T. Tracheal sound analysis using a deep neural network to detect sleep apnea. J Clin Sleep Med. 2019;15(8): 1125-1133.
| S-EPMC6707047 | biostudies-literature
Project description:Nocturnal awakenings are one of the most prevalent sleep disturbances in the general population. Little is known, however, about the frequency of these episodes and how difficulty resuming sleep once awakened affects subjective sleep quality and quantity.This is a cross-sectional telephone study with a representative sample consisting of 8937 non-institutionalized individuals aged 18 or over living in Texas, New York and California. The interviews included questions on sleeping habits, health, sleep and mental disorders. Nocturnal awakenings were evaluated according to their frequency per week and per night, as well as their duration.A total of 35.5% of the sample reported awakening at least three nights per week. Of this 35.5%, 43% (15.2% of the total sample) reported difficulty resuming sleep once awakened. More than 80% of subjects with insomnia symptoms (difficulty initiating or maintaining sleep or non-restorative sleep) also had nocturnal awakenings. Difficulty resuming sleep was associated with subjective shorter sleep duration, poorer sleep quality, greater daytime impairment, greater consultations for sleep disturbances and greater likelihood of receiving a sleep medication.Nocturnal awakenings disrupt the sleep of about one-third of the general population. Using difficulty resuming sleep identifies individuals with significant daytime impairment who are most likely to seek medical help for their sleep disturbances. In the absence of other insomnia symptoms, nocturnal awakenings alone are unlikely to be associated with daytime impairments.
| S-EPMC2830306 | biostudies-literature
Project description:Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30?s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that the algorithm generalizes well to an independent dataset of 993 subjects labeled by American Academy of Sleep Medicine (AASM) licensed clinical staff at Massachusetts General Hospital that was not used for training or validation. Finally, we demonstrate that the stages predicted by our algorithm can reproduce previous clinical studies correlating sleep stages with comorbidities such as sleep apnea and hypertension as well as demographics such as age and gender.
| S-EPMC7441407 | biostudies-literature
Project description:Study objectivesReliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS).MethodsThe ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography.ResultsMean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS.ConclusionsOur results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA.
| S-EPMC6778346 | biostudies-literature
Project description:IntroductionObstructive sleep apnea syndrome (OSAS) is associated with an increased cardiovascular risk. The underlying mechanisms are largely unclear. MicroRNAs (miRNAs) are RNAs circulating in the blood that can be released into the bloodstream during hypoxia. In the present study, we investigate if OSAS-induced hypoxia results in a release of miRNAs that may mediate OSAS-associated cardiovascular damage.MethodsBlood was sampled from 23 OSAS patients before and after a polygraphically monitored night. Total circulating RNA was isolated from the plasma and quantified using real-time qPCR. Using a Taqman miRNA array, the levels of 384 different miRNAs were compared between evening and morning after polysomnography. The most highly upregulated miRNA (miRNA-505) and four additionally upregulated miRNAs (miRNA-127, miRNA-133a, miRNA-145, and miRNA-181a) were then quantified in a bigger patient cohort individually.ResultsApnea/Hypopnea-Index (AHI) was evaluated and averaged at 26 per hour on nocturnal polygraphy. In an initial miRNA array, a total of 4 miRNAs were significantly regulated. A significant increase of miRNA-145 was observed in the larger patient cohort. No significant changes in concentration were detected for miRNA-127, miRNA-133a, miRNA-181a, and miRNA-505 in this larger cohort.ConclusionOSAS results in the nocturnal release of miRNAs into the bloodstream. Our collected data may indicate a hypoxia-induced release of miRNAs into the bloodstream of OSAS-patients. In vitro experiments are needed to confirm the secretion of these miRNAs under hypoxia and evaluate the effect on the cardio vasculature.
| S-EPMC8896679 | biostudies-literature
Project description:Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.
| S-EPMC8050216 | biostudies-literature
Project description:Study Objectives:Automated sleep staging has been previously limited by a combination of clinical and physiological heterogeneity. Both factors are in principle addressable with large data sets that enable robust calibration. However, the impact of sample size remains uncertain. The objectives are to investigate the extent to which machine learning methods can approximate the performance of human scorers when supplied with sufficient training cases and to investigate how staging performance depends on the number of training patients, contextual information, model complexity, and imbalance between sleep stage proportions. Methods:A total of 102 features were extracted from six electroencephalography (EEG) channels in routine polysomnography. Two thousand nights were partitioned into equal (n = 1000) training and testing sets for validation. We used epoch-by-epoch Cohen's kappa statistics to measure the agreement between classifier output and human scorer according to American Academy of Sleep Medicine scoring criteria. Results:Epoch-by-epoch Cohen's kappa improved with increasing training EEG recordings until saturation occurred (n = ~300). The kappa value was further improved by accounting for contextual (temporal) information, increasing model complexity, and adjusting the model training procedure to account for the imbalance of stage proportions. The final kappa on the testing set was 0.68. Testing on more EEG recordings leads to kappa estimates with lower variance. Conclusion:Training with a large data set enables automated sleep staging that compares favorably with human scorers. Because testing was performed on a large and heterogeneous data set, the performance estimate has low variance and is likely to generalize broadly.
| S-EPMC6251659 | biostudies-literature
Project description:Study objectiveValidate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors.MethodsMice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested.ResultsEF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG.ConclusionsEF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.
| S-EPMC7454007 | biostudies-literature
Project description:BACKGROUND:Current sleep medicine nosology places increased importance on nocturnal polysomnographic sleep recordings in the diagnosis of central nervous system disorders of hypersomnolence, particularly idiopathic hypersomnia (IH). OBJECTIVE:Determine what differences in sleep staging and architecture exist between IH and healthy controls using meta-analysis. METHODS:Systematic review identified relevant studies that included nocturnal polysomnography data for IH and healthy control groups. Meta-analysis compared standardized mean differences (Hedge's g) for total sleep time (TST), sleep onset latency (SOL), sleep efficiency (SE), rapid eye movement (REM) sleep percentage, slow wave sleep (SWS) percentage, and REM latency (REML). Moderator analyses were also conducted for variables with significant heterogeneity among studies. RESULTS:The meta-analysis included 10 studies. Relative to controls, IH demonstrated increased TST (pooled g = 0.92; 95% CI: 0.46 to 1.38, p < 0.0001) and REM percentage (pooled g = 0.36, 95% CI: 0.09 to 0.64, p = 0.01), decreased SOL (pooled g = -0.46; 95% CI: -0.81 to -0.12, p = 0.009) and SWS percentage (pooled g = -0.28, 95% CI: -0.50 to -0.07, p = 0.01), without significant differences in SE (pooled g = 0.03; 95% CI: -0.32 to 0.38, p = 0.86) or REML (pooled g = 0.14, 95% CI: -0.21 to 0.49, p = 0.42). Moderator analysis demonstrated a significant effect of sex on SE, with a higher proportion of women to men significantly predicting lower SE between in IH and controls (p < 0.0001). CONCLUSIONS:IH is associated with several changes in sleep staging and architecture relative to healthy persons, including alterations in REM and SWS not currently delineated in nosological constructs. Further research is indicated to clarify how these findings are related the pathophysiology of IH and related disorders.
| S-EPMC5916770 | biostudies-literature
Project description:BackgroundSleep talking (ST) is characterized by the production of unaware verbal vocal activations (VBs) during sleep. ST seems potentially linked to linguistic and memory consolidation processes. However, sleep and dream characteristics and the relationship between verbal vocalizations (VBs) and cognitive functions are still unknown. Our study aimed to investigate qualitative sleep and dream features in sleep talkers (STs) compared to healthy subjects (CNTs) through retrospective and longitudinal measures and explore the relationship between ST and memory consolidation.MethodsWe recruited N = 29 STs and N = 30 CNTs (age range of 18-35). Participants recorded their dreams and filled out sleep logs for seven consecutive days. Vocal activations of STs were audio-recorded. On the eighth day, we administered a word-pair task.ResultsWe showed that STs had significantly worse self-reported sleep quality. VBs were positively correlated with sleep fragmentation and negatively associated with the oneiric emotional load. No difference between groups was found in the memory consolidation rate.ConclusionsAlthough ST is a benign phenomenon, we revealed that ST is associated with more sleep alterations and lower emotional intensity of dreams. In this vein, we support that ST depends on sleep fragmentation and could represent a potential window into sleep-dependent cognitive processes.
| S-EPMC9658338 | biostudies-literature