Density Estimation and Wavelet Thresholding via Bayesian Methods: A Wavelet Probability Band and Related Metrics Approach to Assess Agitation and Sedation in ICU Patients

A wave is usually defined as an oscillating function that is localized in both time and frequency. A wavelet is a small wave, which has its energy concentrated in time providing a tool for the analysis of transient, non-stationary, or time-varying phenomena. Wavelets have the ability to allow simultaneous time and frequency analysis via a flexible mathematical foundation. Wavelets are well suited to the analysis of transient signals in particular. The localizing property of wavelets allows a wavelet expansion of a transient component on an orthogonal basis to be modelled using a small number of wavelet coefficients using a low pass filter. This wavelet paradigm has been applied in a wide range of fields, such as signal processing, data compression and image analysis.

[1]  S L Shafer,et al.  Response Surface Model for Anesthetic Drug Interactions , 2000, Anesthesiology.

[2]  I. Hudson,et al.  Singular Spectrum Analysis: Climatic Niche Identification , 2010 .

[3]  J G Chase,et al.  Physiological modelling of agitation-sedation dynamics. , 2006, Medical engineering & physics.

[4]  B. Vidakovic Nonlinear wavelet shrinkage with Bayes rules and Bayes factors , 1998 .

[5]  G. Fraser,et al.  Monitoring sedation, agitation, analgesia, and delirium in critically ill adult patients. , 2001, Critical care clinics.

[6]  C. Sessler,et al.  The Richmond Agitation-Sedation Scale: validity and reliability in adult intensive care unit patients. , 2002, American journal of respiratory and critical care medicine.

[7]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[8]  Robert D. Nowak,et al.  Wavelet-based statistical signal processing using hidden Markov models , 1998, IEEE Trans. Signal Process..

[9]  H. Chipman,et al.  Adaptive Bayesian Wavelet Shrinkage , 1997 .

[10]  Irene L. Hudson,et al.  Wavelets and clustering: methods to assess synchronization , 2012 .

[11]  M. Clyde,et al.  Flexible empirical Bayes estimation for wavelets , 2000 .

[12]  J. Geoffrey Chase,et al.  A new model validation tool using kernel regression and density estimation , 2005, Comput. Methods Programs Biomed..

[13]  R. Ogden,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 1996 .

[14]  R. Tibshirani,et al.  An introduction to the bootstrap , 1993 .

[15]  Matthew P. Wand,et al.  Kernel Smoothing , 1995 .

[16]  I. Kang,et al.  Wavelet characterization of eucalypt flowering and the influence of climate , 2011, Environmental and Ecological Statistics.

[17]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[18]  I. Johnstone,et al.  Minimax estimation via wavelet shrinkage , 1998 .

[19]  Irene Lena Hudson,et al.  Interdisciplinary approaches: towards new statistical methods for phenological studies , 2010 .

[20]  N. L. Johnson,et al.  Systems of frequency curves generated by methods of translation. , 1949, Biometrika.

[21]  A. Walden,et al.  Wavelet Methods for Time Series Analysis , 2000 .

[22]  B. Silverman,et al.  Wavelet thresholding via a Bayesian approach , 1998 .

[23]  Awad Al-Asmari Discrete Wavelet Transforms - A Compendium of New Approaches and Recent Applications , 2013 .

[24]  In Kang Wavelets, ICA and statistical parametric mapping : with application to agitation-sedation modelling, detecting change points & to neuroinformatics , 2011 .

[25]  J. Engel Density estimation with Haar series , 1990 .

[26]  Praveen Kumar,et al.  A multicomponent decomposition of spatial rainfall fields: 1. Segregation of large‐ and small‐scale features using wavelet transforms , 1993 .

[27]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[28]  M. Ramsay,et al.  Controlled Sedation with Alphaxalone-Alphadolone , 1974, British medical journal.

[29]  Martin Vetterli,et al.  Spatially adaptive wavelet thresholding with context modeling for image denoising , 2000, IEEE Trans. Image Process..

[30]  Trevor Coward,et al.  Nova Science Publishers , 2013 .

[31]  A. Jaarsma,et al.  Biphasic positive airway pressure ventilation (PeV+) in children , 2001, Critical care.

[32]  G. A Theory for Multiresolution Signal Decomposition : The Wavelet Representation , 2004 .

[33]  François G. Meyer Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series , 2003, IEEE Transactions on Medical Imaging.

[34]  Geoffrey M. Shaw,et al.  Impact of control on agitation–sedation dynamics , 2005 .

[35]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[36]  Hugh A. Chipman,et al.  Prior Elicitation in the Wavelet Domain , 1999 .

[37]  A. Grossmann,et al.  Cycle-octave and related transforms in seismic signal analysis , 1984 .

[38]  M. Clyde,et al.  Multiple shrinkage and subset selection in wavelets , 1998 .

[39]  Norbert Weiler,et al.  Sedation and analgesia in the intensive care unit , 2003, Current opinion in anaesthesiology.

[40]  R. Gencay,et al.  An Introduction to Wavelets and Other Filtering Methods in Finance and Economics , 2001 .

[41]  Stuart Barber,et al.  Posterior probability intervals for wavelet thresholding , 2002 .

[42]  J. Geoffrey Chase,et al.  Wavelet Signatures and Diagnostics for the Assessment of ICU Agitation-Sedation Protocols , 2011 .

[43]  Praveen Kumar,et al.  Role of coherent structures in the stochastic-dynamic variability of precipitation , 1996 .

[44]  D. Walnut An Introduction to Wavelet Analysis , 2004 .

[45]  Guy P. Nason,et al.  Wavelet Methods in Statistics with R , 2008 .

[46]  I. D. Hill,et al.  Fitting Johnson Curves by Moments , 1976 .

[47]  I. Kang,et al.  Wavelet signatures of climate and flowering: identification of species groupings , 2011 .

[48]  Irene L. Hudson,et al.  Phenological research: Methods for environmental and climate change analysis , 2010 .

[49]  L D Cromwell,et al.  Filtering noise from images with wavelet transforms , 1991, Magnetic resonance in medicine.

[50]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[51]  I. Kang,et al.  Wavelet Analysis of Flowering and Climatic Niche Identification , 2010 .

[52]  Sujit K. Ghosh,et al.  Essential Wavelets for Statistical Applications and Data Analysis , 2001, Technometrics.

[53]  Dominic S. Lee,et al.  Modeling and control of the agitation-sedation cycle for critical care patients. , 2004, Medical engineering & physics.

[54]  T. Hettmansperger,et al.  Robust Nonparametric Statistical Methods , 1998 .

[55]  I. D. Hill Algorithm AS 100: Normal-Johnson and Johnson-Normal Transformations , 1976 .

[56]  G. Fraser,et al.  Prospective evaluation of the Sedation-Agitation Scale for adult critically ill patients. , 1999, Critical care medicine.

[57]  R. V. Sachs,et al.  Nonparametric stochastic regression with design-adapted wavelets , 2001 .

[58]  J. Morlet Sampling Theory and Wave Propagation , 1983 .

[59]  J G Chase,et al.  Physiological modelling of agitation-sedation dynamics including endogenous agitation reduction. , 2006, Medical engineering & physics.