Snoring and sleep apnea in children

Snoring is a primary and major clinical symptom of upper airway obstruction during sleep. Sleep-disordered breathing ranges from primary snoring to significant partial upper airway obstruction, and obstructive sleep apnea. Adult snoring and obstructive sleep apnea have been extensively studied, whereas less is known about these disorders in children. Snoring and more severe obstructive sleep apnea have been shown to have a harmful effect on the neurobehavioral development of children, but the mechanisms of this effect remains unknown. Furthermore, the correlation of this effect to objective sleep study parameters remains poor. This study evaluated the prevalence of snoring in preschool-aged children in Finland. Host and environmental risk factors, and neurobehavioral and neurocognitive symptoms of children suffering from snoring or obstructive sleep apnea were also investigated. The feasibility of acoustic rhinometry in young children was assessed. The prevalence and risk factors of snoring (I) were evaluated by a questionnaire. The random sample included 2100 children aged 1-6 years living in Helsinki. All 3- to 6-year-old children whose parents reported their child to snore always, often, or sometimes were categorized as snorers, and invited to participate to the clinical study (II-IV). Non-snoring children whose parents were willing to participate in the clinical study were invited to serve as controls. Children underwent a clinical ear-nose-throat examination. Emotional, behavioral, and cognitive performances were evaluated by Child Behavioral Checklist (CBCL), Wechsler Preschool and Primary Scale of Intelligence (WPPSI-R) and NEPSY-A Developmental Neuropsychological Assessment (NEPSY). Nasal volume was measured by acoustic rhinometry, and nasal resistance by rhinomanometry. Lateral and posteroanterior cephalometry were performed. A standard overnight ambulatory polysomnography was performed in the home environment. Twenty-six healthy children were tested in order to assess the feasibility of acoustic rhinometry in young children (V). Snoring was common in children; 6.3% of children snored always or often, whereas 81.3% snored never or occasionally. No differences were apparent between snorers and non-snorers regarding age, or gender. Pediatric snoring was associated with recurrent upper respiratory infections, otitis media, and allergic rhinitis. Exposure to parental tobacco smoke, especially maternal smoking, was more common among snorers. Rhinitis was more common among children who exposured to tobacco smoke. Overnight polysomnography (PSG) was performed on 87 children; 74% showed no signs of significant upper airway obstruction during sleep. Three children had obstructive apnea/hypopnea index (OAHI) greater than 5/h. Age, gender, or a previous adenoidectomy or tonsillectomy did not correlate with OAHI, whereas tonsillar size did correlate with OAHI. Relative body weight and obesity correlated with none of the PSG parameters. In cephalometry, no clear differences or correlations were found in PSG parameters or between snorers and non-snorers. No correlations were observed between acoustic rhinometry, rhinomanometry, and PSG parameters. Psychiatric symptoms were more frequent in the snoring group than in the nonsnoring group. In particular, anxious and depressed symptoms were more prevalent in the snoring group. Snoring children frequently scored lower in language functions. However, PSG parameters correlated poorly with neurocognitive test results in these children. This study and previous studies indicate that snoring without episodes of obstructive apnea or SpO2 desaturations may cause impairment in behavioral and neurocognitive functions. The mechanism of action remains unknown. Exposure to parental tobacco smoke is more common among snorers than non-snorers, emphasizing the importance of a smoke-free environment. Children tolerated acoustic rhinometry measurements well.

Tunable Locally-Optimal Geographical Forwarding in Wireless Sensor Networks with Sleep-Wake Cycling Nodes

We consider a wireless sensor network whose main function is to detect certain infrequent alarm events, and to forward alarm packets to a base station, using geographical forwarding. The nodes know their locations, and they sleep-wake cycle, waking up periodically but not synchronously. In this situation, when a node has a packet to forward to the sink, there is a trade-off between how long this node waits for a suitable neighbor to wake up and the progress the packet makes towards the sink once it is forwarded to this neighbor. Hence, in choosing a relay node, we consider the problem of minimizing average delay subject to a constraint on the average progress. By constraint relaxation, we formulate this next hop relay selection problem as a Markov decision process (MDP). The exact optimal solution (BF (Best Forward)) can be found, but is computationally intensive. Next, we consider a mathematically simplified model for which the optimal policy (SF (Simplified Forward)) turns out to be a simple one-step-look-ahead rule. Simulations show that SF is very close in performance to BF, even for reasonably small node density. We then study the end-to-end performance of SF in comparison with two extremal policies: Max Forward (MF) and First Forward (FF), and an end-to-end delay minimising policy proposed by Kim et al. 1]. We find that, with appropriate choice of one hop average progress constraint, SF can be tuned to provide a favorable trade-off between end-to-end packet delay and the number of hops in the forwarding path.

Optimal Sleep-Wake Scheduling for Quickest Intrusion Detection using wireless sensor Networks

We consider the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active. By using a minimal number of sensor devices,we ensure that the energy expenditure for sensing, computation and communication is minimized (and the lifetime of the network is maximized). We model the intrusion detection (or change detection) problem as a Markov decision process (MDP). Based on the theory of MDP, we develop the following closed loop sleep/wake scheduling algorithms: 1) optimal control of Mk+1, the number of sensors in the wake state in time slot k + 1, 2) optimal control of qk+1, the probability of a sensor in the wake state in time slot k + 1, and an open loop sleep/wake scheduling algorithm which 3) computes q, the optimal probability of a sensor in the wake state (which does not vary with time),based on the sensor observations obtained until time slot k.Our results show that an optimum closed loop control onMk+1 significantly decreases the cost compared to keeping any number of sensors active all the time. Also, among the three algorithms described, we observe that the total cost is minimum for the optimum control on Mk+1 and is maximum for the optimum open loop control on q.

Relay Selection for Geographical Forwarding in Sleep-Wake Cycling Wireless Sensor Networks

Our work is motivated by geographical forwarding of sporadic alarm packets to a base station in a wireless sensor network (WSN), where the nodes are sleep-wake cycling periodically and asynchronously. We seek to develop local forwarding algorithms that can be tuned so as to tradeoff the end-to-end delay against a total cost, such as the hop count or total energy. Our approach is to solve, at each forwarding node enroute to the sink, the local forwarding problem of minimizing one-hop waiting delay subject to a lower bound constraint on a suitable reward offered by the next-hop relay; the constraint serves to tune the tradeoff. The reward metric used for the local problem is based on the end-to-end total cost objective (for instance, when the total cost is hop count, we choose to use the progress toward sink made by a relay as the reward). The forwarding node, to begin with, is uncertain about the number of relays, their wake-up times, and the reward values, but knows the probability distributions of these quantities. At each relay wake-up instant, when a relay reveals its reward value, the forwarding node's problem is to forward the packet or to wait for further relays to wake-up. In terms of the operations research literature, our work can be considered as a variant of the asset selling problem. We formulate our local forwarding problem as a partially observable Markov decision process (POMDP) and obtain inner and outer bounds for the optimal policy. Motivated by the computational complexity involved in the policies derived out of these bounds, we formulate an alternate simplified model, the optimal policy for which is a simple threshold rule. We provide simulation results to compare the performance of the inner and outer bound policies against the simple policy, and also against the optimal policy when the source knows the exact number of relays. Observing the good performance and the ease of implementation of the simple policy, we apply it to our motivating problem, i.e., local geographical routing of sporadic alarm packets in a large WSN. We compare the end-to-end performance (i.e., average total delay and average total cost) obtained by the simple policy, when used for local geographical forwarding, against that obtained by the globally optimal forwarding algorithm proposed by Kim et al. 1].

Two timescale convergent Q-learning for sleep-scheduling in wireless sensor networks

In this paper, we consider an intrusion detection application for Wireless Sensor Networks. We study the problem of scheduling the sleep times of the individual sensors, where the objective is to maximize the network lifetime while keeping the tracking error to a minimum. We formulate this problem as a partially-observable Markov decision process (POMDP) with continuous stateaction spaces, in a manner similar to Fuemmeler and Veeravalli (IEEE Trans Signal Process 56(5), 2091-2101, 2008). However, unlike their formulation, we consider infinite horizon discounted and average cost objectives as performance criteria. For each criterion, we propose a convergent on-policy Q-learning algorithm that operates on two timescales, while employing function approximation. Feature-based representations and function approximation is necessary to handle the curse of dimensionality associated with the underlying POMDP. Our proposed algorithm incorporates a policy gradient update using a one-simulation simultaneous perturbation stochastic approximation estimate on the faster timescale, while the Q-value parameter (arising from a linear function approximation architecture for the Q-values) is updated in an on-policy temporal difference algorithm-like fashion on the slower timescale. The feature selection scheme employed in each of our algorithms manages the energy and tracking components in a manner that assists the search for the optimal sleep-scheduling policy. For the sake of comparison, in both discounted and average settings, we also develop a function approximation analogue of the Q-learning algorithm. This algorithm, unlike the two-timescale variant, does not possess theoretical convergence guarantees. Finally, we also adapt our algorithms to include a stochastic iterative estimation scheme for the intruder's mobility model and this is useful in settings where the latter is not known. Our simulation results on a synthetic 2-dimensional network setting suggest that our algorithms result in better tracking accuracy at the cost of only a few additional sensors, in comparison to a recent prior work.

Adaptive Sleep-Wake Control using Reinforcement Learning in Sensor Networks

The aim in this paper is to allocate the `sleep time' of the individual sensors in an intrusion detection application so that the energy consumption from the sensors is reduced, while keeping the tracking error to a minimum. We propose two novel reinforcement learning (RL) based algorithms that attempt to minimize a certain long-run average cost objective. Both our algorithms incorporate feature-based representations to handle the curse of dimensionality associated with the underlying partially-observable Markov decision process (POMDP). Further, the feature selection scheme used in our algorithms intelligently manages the energy cost and tracking cost factors, which in turn assists the search for the optimal sleeping policy. We also extend these algorithms to a setting where the intruder's mobility model is not known by incorporating a stochastic iterative scheme for estimating the mobility model. The simulation results on a synthetic 2-d network setting are encouraging.

Relay Selection with Channel Probing in Sleep-Wake Cycling Wireless Sensor Networks

In geographical forwarding of packets in a large wireless sensor network (WSN) with sleep-wake cycling nodes, we are interested in the local decision problem faced by a node that has ``custody'' of a packet and has to choose one among a set of next-hop relay nodes to forward the packet toward the sink. Each relay is associated with a ``reward'' that summarizes the benefit of forwarding the packet through that relay. We seek a solution to this local problem, the idea being that such a solution, if adopted by every node, could provide a reasonable heuristic for the end-to-end forwarding problem. Toward this end, we propose a local relay selection problem consisting of a forwarding node and a collection of relay nodes, with the relays waking up sequentially at random times. At each relay wake-up instant, the forwarder can choose to probe a relay to learn its reward value, based on which the forwarder can then decide whether to stop (and forward its packet to the chosen relay) or to continue to wait for further relays to wake up. The forwarder's objective is to select a relay so as to minimize a combination of waiting delay, reward, and probing cost. The local decision problem can be considered as a variant of the asset selling problem studied in the operations research literature. We formulate the local problem as a Markov decision process (MDP) and characterize the solution in terms of stopping sets and probing sets. We provide results illustrating the structure of the stopping sets, namely, the (lower bound) threshold and the stage independence properties. Regarding the probing sets, we make an interesting conjecture that these sets are characterized by upper bounds. Through simulation experiments, we provide valuable insights into the performance of the optimal local forwarding and its use as an end-to-end forwarding heuristic.

The C. elegans ALA neuron : its transcriptome and role in inducing sleep

A long-standing yet to be accomplished task in understanding behavior is to dissect the function of each gene involved in the development and function of a neuron. The C. elegans ALA neuron was chosen in this study for its known function in sleep, an ancient but less understood animal behavior. Single-cell transcriptome profiling identified 8,133 protein-coding genes in the ALA neuron, of which 57 are neuropeptide-coding genes. The most enriched genes are also neuropeptides. In combination with gain-of-function and loss-of-function assays, here I showed that the ALA-enriched FMRFamide neuropeptides, FLP-7, FLP-13, and FLP-24, are sufficient and necessary for inducing C. elegans sleep. These neuropeptides act as neuromodulators through GPCRs, NPR-7, and NPR-22. Further investigation in zebrafish indicates that FMRFamide neuropeptides are sleep-promoting molecules in animals. To correlate the behavioral outputs with genomic context, I constructed a gene regulatory network of the relevant genes controlling C. elegans sleep behavior through EGFR signaling in the ALA neuron. First, I identified an ALA cell-specific motif to conduct a genome-wide search for possible ALA-expressed genes. I then filtered out non ALA-expressed genes by comparing the motif-search genes with ALA transcriptomes from single-cell profiling. In corroborating with ChIP-seq data from modENCODE, I sorted out direct interaction of ALA-expressed transcription factors and differentiation genes in the EGFR sleep regulation pathway. This approach provides a network reference for the molecular regulation of C. elegans sleep behavior, and serves as an entry point for the understanding of functional genomics in animal behaviors.

Inflammatory Markers and Obstructive Sleep Apnea in Obese Children: The NANOS Study

Introduction. Obesity and obstructive sleep apnea syndrome (OSA) are common coexisting conditions associated with a chronic low-grade inflammatory state underlying some of the cognitive, metabolic, and cardiovascular morbidities. Aim. To examine the levels of inflammatory markers in obese community-dwelling children with OSA, as compared to no-OSA, and their association with clinical and polysomnographic (PSG) variables. Methods. In this cross-sectional, prospective multicenter study, healthy obese Spanish children (ages 4-15 years) were randomly selected and underwent nocturnal PSG followed by a morning fasting blood draw. Plasma samples were assayed for multiple inflammatory markers. Results. 204 children were enrolled in the study; 75 had OSA, defined by an obstructive respiratory disturbance index (RDI) of 3 events/hour total sleep time (TST). BMI, gender, and age were similar in OSA and no-OSA children. Monocyte chemoattractant protein-1 (MCP-1) and plasminogen activator inhibitor-1 (PAI-1) levels were significantly higher in OSA children, with interleukin-6 concentrations being higher in moderate-severe OSA (i.e., AHI > 5/hrTST; P < 0.01), while MCP-1 levels were associated with more prolonged nocturnal hypercapnia (P < 0.001). Conclusion. IL-6, MCP-1, and PAI-1 are altered in the context of OSA among community-based obese children further reinforcing the proinflammatory effects of sleep disorders such as OSA. This trial is registered with ClinicalTrials.gov NCT01322763.

Differential amplitude modulation of auditory evoked cortical potentials associated with brain state in the freely moving rhesus monkey

We simultaneously recorded auditory evoked potentials (AEP) from the temporal cortex (TCx), the dorsolateral prefrontal cortex (dPFCx) and the parietal cortex (PCx) in the freely moving rhesus monkey to investigate state-dependent changes of the AEP. AEPs obtained during passive wakefulness, active wakefulness (AW), slow wave sleep and rapid-eye-movement sleep (REM) were compared. Results showed that AEP from all three cerebral areas were modulated by brain states. However, the amplitude of AEP from dPFCx and PCx significantly appeared greater attenuation than that from the TCx during AW and REM. These results indicate that the modulation of brain state on AEP from all three cerebral areas investigated is not uniform, which suggests that different cerebral areas have differential functional contributions during sleep-wake cycle. (C) 2002 Elsevier Science Ireland Ltd.. All rights reserved.

Sleep-disordered breathing and orthodontic variables in children-Pilot study

Introduction: Sleep Disordered Breathing (SDB) is a highly prevalent condition associated with orofacial and dentofacial characteristics.Objective: the aim of this study was to verify the association of dental malocclusion, molar relationship, crossbite, open bite, overjet, overbite, and crowding with SDB in children aged 7-9 years.Materials and methods: Participating schools were selected randomly from within the public elementary school system. in the first phase of the study, the parents of 1216 children aged between 7 and 9 years old completed the Sleep Disturbance Scale for Children (SDSC) questionnaire and the children had to participate in a dental examination. the evaluation of occlusion was divided into sagittal analysis, vertical analysis, and transverse analysis. in the second phase, 60 children were selected randomly to be undergone polysomnography (PSG) at a sleep clinic.Results: Among the children included, 242 (19.9%) children had normal occlusion. of the 60 children, 50 underwent PSG and 40(80%) had SDB. the crossbite and open bite showed association with SDB, p = 0.04 in both.Conclusion: Crossbite and open bite malocclusions were associated with SDB, and may be predictive of SDB in children. Studies with larger numbers of participants are needed to investigate the association of other malocclusions with SDB, and randomized clinical trials are also needed to see whether orthodontic and/or functional jaw orthopedic treatment is an option for treating children with malocclusion and SDB. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

Sleep Patterns in Elementary School Children (Grades 2-5) and Adolescents (Grade 10)

Background: To date, there is limited research examining sleep patterns in elementary school children. Previous researchers focused on parental responses rather than student responses to determine factors that affect sleep. The presented study surveyed sleep patterns and examined external factors affecting total sleep time among elementary school children and adolescents. Methods: Students in grades 2-5 (n=885) and grade 10 (n=190) enrolled in a public school system in the Northeast, completed a district administered survey that included questions on sleep duration and hygiene. Results. Average reported sleep duration decreased with increasing grade level. Children in grades 2-5 woke up earlier (31.7-72.4%) and on their own in comparison to adolescents in grade 10 (6.8%). Significantly shorter sleep durations were associated with having a television (grades 2, 4, 5, p< 0.01) or a cell phone in the room (grades 3, 4; p < 0.05), playing on the computer or video games (grades 3, 4, p<.001) before going to bed. In contrast, students in grade 2, 3, & 4 who reported reading a book before going to bed slept on average 21 minutes more per night (p=.029, .007, .009, respectively). For tenth graders, only consumption of energy drinks led to significant reduction in sleep duration (p<.0001). Conclusion. Sleep is a fundamental aspect in maintaining a healthy and adequate life style. Understanding sleep patterns will assist parents, health care providers, and educators in promoting quality sleep hygiene in school-aged children.

Spike avalanches exhibit universal dynamics across the sleep-wake cycle.

BACKGROUND: Scale-invariant neuronal avalanches have been observed in cell cultures and slices as well as anesthetized and awake brains, suggesting that the brain operates near criticality, i.e. within a narrow margin between avalanche propagation and extinction. In theory, criticality provides many desirable features for the behaving brain, optimizing computational capabilities, information transmission, sensitivity to sensory stimuli and size of memory repertoires. However, a thorough characterization of neuronal avalanches in freely-behaving (FB) animals is still missing, thus raising doubts about their relevance for brain function. METHODOLOGY/PRINCIPAL FINDINGS: To address this issue, we employed chronically implanted multielectrode arrays (MEA) to record avalanches of action potentials (spikes) from the cerebral cortex and hippocampus of 14 rats, as they spontaneously traversed the wake-sleep cycle, explored novel objects or were subjected to anesthesia (AN). We then modeled spike avalanches to evaluate the impact of sparse MEA sampling on their statistics. We found that the size distribution of spike avalanches are well fit by lognormal distributions in FB animals, and by truncated power laws in the AN group. FB data surrogation markedly decreases the tail of the distribution, i.e. spike shuffling destroys the largest avalanches. The FB data are also characterized by multiple key features compatible with criticality in the temporal domain, such as 1/f spectra and long-term correlations as measured by detrended fluctuation analysis. These signatures are very stable across waking, slow-wave sleep and rapid-eye-movement sleep, but collapse during anesthesia. Likewise, waiting time distributions obey a single scaling function during all natural behavioral states, but not during anesthesia. Results are equivalent for neuronal ensembles recorded from visual and tactile areas of the cerebral cortex, as well as the hippocampus. CONCLUSIONS/SIGNIFICANCE: Altogether, the data provide a comprehensive link between behavior and brain criticality, revealing a unique scale-invariant regime of spike avalanches across all major behaviors.

Chronotype, sleep quality and sleep duration in adult distance education: Not related to study progress

Research in traditional education shows chronotype, sleep duration and sleep quality to be related to learning performance. Research in adult students participating in distance education (DE) is scarce. This study aims to provide knowledge on these relationships in this educational setting. In an observational longitudinal study, chronotype, sleep duration (i.e., for work and free days separately) and sleep quality of 894 students were analyzed in a multiple regression analyses. Students provided information on sleep-relatedmeasures and important covariates at the start of their study and study progress was evaluated after 14 months (i.e., the number of successfully completed modules). In linewith previous research, chronotype did not predict study progress. Further, sleep duration did not predict study progress, neither as a linear nor as a polynomial term. Third, sleep quality did not predict study progress. Concluding, these results are in linewith previous research that DE provides a solution to the asynchrony problem. Findings regarding sleep duration and sleep quality are new and unexpected, asking for attention and further research. Despite the study's observational nature, findings suggest that students participating in DE may benefit from this type of education as the asynchrony problem appears not to apply here, as students can choose their own study schedule.