3,813 research outputs found

    Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

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    The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep

    Modeling person-to-person contaminant transport in enclosed environments

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    It is essential to predict person-to-person contaminant transport in enclosed environments to improve air distribution design and reduce the infection risk from airborne infectious diseases. This study aims to improve and accelerate the simulation of person-to-person contaminant transport in enclosed environments. ^ This investigation first conducted experimental measurements of person-to-person contaminant transport in an office mockup and the first-class cabin of a functional MD-82 aircraft. The experimental data of steady-state airflow, temperature, and gas contaminant concentration fields obtained in the office were used to validate the steady-state computational fluid dynamics (CFD) models. In the aircraft cabin, the transient particle concentrations were measured at the breathing zones of each passenger. The experimental data were used for evaluating the transient particle models in this study. ^ When applying the CFD models, most of the existing studies assumed that the index person coughed or sneezed directly without covering the mouth. In reality, however, people usually cover their mouths with a hand or a tissue when they cough or sneeze. Currently, no simple method is available in the literature for predicting the exhaled airflow from a cough with the mouth covered. Therefore, this study developed simplified models for predicting the airflow on the basis of the smoke visualization experiment. This investigation then applied the developed simplified models to assess the influence of mouth coverings on the receptor\u27s exposure to exhaled particles. It was found that covering a cough with a tissue, a cupped hand, or an elbow can significantly reduce the horizontal transport of exhaled particles. ^ As a popular particle model, the Lagrangian model needs to track a large number of particles in the calculations in order to ensure accuracy. Traditionally, modelers have conducted an independence test in order to find a reasonable value for this particle number. However, the unguided process of an independence test can be highly time-consuming. Therefore, this investigation developed a method for estimating the necessary particle number in the Lagrangian model. The results show that the proposed method can estimate the necessary particle number with a reasonable magnitude and thus reduce the effort that is normally required for evaluating different numbers of particles in order to achieve statistically meaningful results. Moreover, the superimposition and time-averaging method was proposed, which can reduce the necessary particle number, and, as a result, the computing cost can be further reduced. ^ Although the traditional Eulerian and Lagrangian models can provide informative results of transient particle transport indoors, they are considerably time-consuming. Thus, this study further developed a new particle model on the basis of a Markov chain frame for quickly predicting transient particle transport indoors. When solving the particle transport equations, the Markov chain model does not require iterations in each time step, and thus it can significantly reduce the computing cost. The validation results show that, in general, the trends in the transient particle concentration distributions predicted by the Markov chain model agreed reasonably well with the experimental data. Furthermore, the Markov chain model produced similar results to those of the Lagrangian and Eulerian models, while the speed of calculation increased by at least 6 times in comparison to the latter two models for the studied case. ^ To further identify a suitable model for indoor transient particle transport simulations, this study systematically compared the Eulerian, Lagrangian, and Markov chain models in terms of performance, computing cost, and robustness. This investigation used four cases, including three cases with experimental data, for the comparison. The comparison shows that all the three models can predict transient particle transport in enclosed environments with a similar accuracy. With the same time step size and grid number, the Markov chain model was the fastest among the three models. Unless super-find grid was used, the Eulerian model was faster than the Lagrangian model. The Eulerian and Lagrangian models were more robust than the Markov chain model, because the Markov chain model was sensitive to the time step size

    Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation

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    Clinical experts assess the newborn brain development by analyzing and interpreting maturity-related features in sleep EEGs. Typically, these features widely vary during the sleep hours, and their informativeness can be different in different sleep stages. Normally, the level of muscle and electrode artifacts during the active sleep stage is higher than that during the quiet sleep that could reduce the informative-ness of features extracted from the active stage. In this paper, we use the methodology of Bayesian averaging over Decision Trees (DTs) to assess the newborn brain maturity and explore the informativeness of EEG features extracted from different sleep stages. This methodology has been shown providing the most accurate inference and estimates of uncertainty, while the use of DT models enables to find the EEG features most important for the brain maturity assessment

    Exploring Cognitive States: Methods for Detecting Physiological Temporal Fingerprints

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    Cognitive state detection and its relationship to observable physiologically telemetry has been utilized for many human-machine and human-cybernetic applications. This paper aims at understanding and addressing if there are unique psychophysiological patterns over time, a physiological temporal fingerprint, that is associated with specific cognitive states. This preliminary work involves commercial airline pilots completing experimental benchmark task inductions of three cognitive states: 1) Channelized Attention (CA); 2) High Workload (HW); and 3) Low Workload (LW). We approach this objective by modeling these "fingerprints" through the use of Hidden Markov Models and Entropy analysis to evaluate if the transitions over time are complex or rhythmic/predictable by nature. Our results indicate that cognitive states do have unique complexity of physiological sequences that are statistically different from other cognitive states. More specifically, CA has a significantly higher temporal psychophysiological complexity than HW and LW in EEG and ECG telemetry signals. With regards to respiration telemetry, CA has a lower temporal psychophysiological complexity than HW and LW. Through our preliminary work, addressing this unique underpinning can inform whether these underlying dynamics can be utilized to understand how humans transition between cognitive states and for improved detection of cognitive states

    EXPERIMENTAL-COMPUTATIONAL ANALYSIS OF VIGILANCE DYNAMICS FOR APPLICATIONS IN SLEEP AND EPILEPSY

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    Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. Notable progress in neuroscience research—and particularly sleep and epilepsy—has been achieved through experimentation on animals. Experimental models of epilepsy provide us with the opportunity to explore or even manipulate the sleep-seizure relationship in order to decipher different aspects of their interactions. Important in this process is the development of techniques for modeling and tracking sleep dynamics using electrophysiological measurements. In this dissertation experimental and computational approaches are proposed for modeling vigilance dynamics and their utility demonstrated in nonepileptic control mice. The general framework of hidden Markov models is used to automatically model and track sleep state and dynamics from electrophysiological as well as novel motion measurements. In addition, a closed-loop sensory stimulation technique is proposed that, in conjunction with this model, provides the means to concurrently track and modulate 3 vigilance dynamics in animals. The feasibility of the proposed techniques for modeling and altering sleep are demonstrated for experimental applications related to epilepsy. Finally, preliminary data from a mouse model of temporal lobe epilepsy are employed to suggest applications of these techniques and directions for future research. The methodologies developed here have clear implications the design of intelligent neuromodulation strategies for clinical epilepsy therapy

    Bayesian testing of many hypotheses ×\times many genes: A study of sleep apnea

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    Substantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (e.g., diseased vs. nondiseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes. However, many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS241 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
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