Monitoring sleep depth: analysis of bispectral index (BIS) based on polysomnographic recordings and sleep deprivation

© 2015 Springer Science+Business Media Dordrecht The assessment and management of sleep are increasingly recommended in the clinical practice. Polysomnography (PSG) is considered the gold standard test to monitor sleep objectively, but some practical and technical constraints exist due to environmental and patient considerations. Bispectral index (BIS) monitoring is commonly used in clinical practice for guiding anesthetic administration and provides an index based on relationships between EEG components. Due to similarities in EEG synchronization between anesthesia and sleep, several studies have assessed BIS as a sleep monitor with contradictory results. The aim of this study was to evaluate objectively both the feasibility and reliability of BIS for sleep monitoring through a robust methodology, which included full PSG recordings at a baseline situation and after 40 h of sleep deprivation. Results confirmed that the BIS index was highly correlated with the hypnogram (0.89 ± 0.02), showing a progressive decrease as sleep deepened, and an increase during REM sleep (awake: 91.77 ± 8.42; stage N1: 83.95 ± 11.05; stage N2: 71.71 ± 11.99; stage N3: 42.41 ± 9.14; REM: 80.11 ± 8.73). Mean and median BIS values were lower in the post-deprivation night than in the baseline night, showing statistical differences for the slow wave sleep (baseline: 42.41 ± 9.14 vs. post-deprivation: 39.49 ± 10.27; p = 0.02). BIS scores were able to discriminate properly between deep (N3) and light (N1, N2) sleep. BIS values during REM overlapped those of other sleep stages, although EMG activity provided by the BIS monitor could help to identify REM sleep if needed. In conclusion, BIS monitors could provide a useful measure of sleep depth in especially particular situations such as intensive care units, and they could be used as an alternative for sleep monitoring in order to reduce PSG-derived costs and to increase capacity in ambulatory care.Postprint (author's final draft

A Nobel Approach for Entropy Reduction of Wireless Sensor Networks (WSN)

In contrast to RF, optical devices are smaller and consume less power; reflection, diffraction, and scattering from aerosols help distribute signal over large areas; and optical wireless provides freedom from interference and eavesdropping within an opaque enclosure. For a densely deployed Wireless Multimedia Sensor Network (WMSN), an entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. The limitations of limited energy, processing power and bandwidth capabilities of sensors networks become critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. Data aggregation is considered to be an effective technique. Selective use of informative sensors reduces the number of sensors needed to obtain information about the target state and therefore prolongs the system lifetime. In this paper the use of entropy in spectrum sensing is also described. This sensing gives knowledge about the usage of spectrum by primary user and based on that a secondary user can utilize the unused spectrum without interfere the primary user

Identifying nonlinear wave interactions in plasmas using two-point measurements: a case study of Short Large Amplitude Magnetic Structures (SLAMS)

A framework is described for estimating Linear growth rates and spectral energy transfers in turbulent wave-fields using two-point measurements. This approach, which is based on Volterra series, is applied to dual satellite data gathered in the vicinity of the Earth's bow shock, where Short Large Amplitude Magnetic Structures (SLAMS) supposedly play a leading role. The analysis attests the dynamic evolution of the SLAMS and reveals an energy cascade toward high-frequency waves.Comment: 26 pages, 13 figure

New Optimised Estimators for the Primordial Trispectrum

Cosmic microwave background studies of non-Gaussianity involving higher-order multispectra can distinguish between early universe theories that predict nearly identical power spectra. However, the recovery of higher-order multispectra is difficult from realistic data due to their complex response to inhomogeneous noise and partial sky coverage, which are often difficult to model analytically. A traditional alternative is to use one-point cumulants of various orders, which collapse the information present in a multispectrum to one number. The disadvantage of such a radical compression of the data is a loss of information as to the source of the statistical behaviour. A recent study by Munshi & Heavens (2009) has shown how to define the skew spectrum (the power spectra of a certain cubic field, related to the bispectrum) in an optimal way and how to estimate it from realistic data. The skew spectrum retains some of the information from the full configuration-dependence of the bispectrum, and can contain all the information on non-Gaussianity. In the present study, we extend the results of the skew spectrum to the case of two degenerate power-spectra related to the trispectrum. We also explore the relationship of these power-spectra and cumulant correlators previously used to study non-Gaussianity in projected galaxy surveys or weak lensing surveys. We construct nearly optimal estimators for quick tests and generalise them to estimators which can handle realistic data with all their complexity in a completely optimal manner. We show how these higher-order statistics and the related power spectra are related to the Taylor expansion coefficients of the potential in inflation models, and demonstrate how the trispectrum can constrain both the quadratic and cubic terms.Comment: 19 pages, 2 figure

Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Bispectral reconstruction of speckle-degraded images

The bispectrum of a signal has useful properties such as being zero for a Gaussian random process, retaining both phase and magnitude information of the Fourier transform of a signal, and being insensitive to linear motion. It has found applications in a wide variety of fields. The use of these properties for reducing speckle in coherent imaging systems was investigated. It was found that the bispectrum could be used to restore speckle-degraded images. Coherent speckle noise is modeled as a multiplicative noise process. By using a logarithmic transformation, this speckle noise is converted to a signal independent, additive process which is close to Gaussian when an integrating aperture is used. Bispectral reconstruction of speckle-degraded images is performed on such logarithmically transformed images when we have independent multiple snapshots