Literature DB >> 17557421

Digital analysis and technical specifications.

Thomas Penzel1, Max Hirshkowitz, John Harsh, Ron D Chervin, Nic Butkov, Meir Kryger, Beth Malow, Michael V Vitiello, Michael H Silber, Clete A Kushida, Andrew L Chesson.   

Abstract

Digital acquisition and analysis of sleep data has become more common over the past 20 years. Many investigators have developed strategies to record and analyze sleep in a quantitative way. Initially, digital recording and analysis were restricted by technical limitations. With current technology, the technical limitations of computer acquisition, data storage, and analysis are less constraining, and the development of recommendations for the specifications and scoring of sleep can be more clearly guided by the goal of characterizing physiologic phenomena. In order to develop recommendations and specifications regarding digital acquisition and analysis, a literature search, evidence review, and standardized consensus process focused on 5 questions regarding computer-assisted sleep recording and analysis. These questions included: (1) the reliability of computerized scoring of sleep stages, (2) the analysis of elemental events and waveforms, (3) the physiological and/or clinical significance of digitally-analyzed signals, (4) the importance of proposed changes in standardized scoring that could incorporate digital analysis, and (5) the potential advantages and disadvantages of computerized sleep recordings. Of 154 studies identified by the search, 119 were found to be suitable for evidence review. The evidence review suggested that computer scoring and quantitative analysis of sleep is still in the formative stage of development. For many technical specification decisions, little or no direct evidence was found, although basic engineering principles or standard practices provided some rationale which was utilized to develop the recommendations formulated during the subsequent UCLA/Rand standardized consensus process.

Mesh:

Year:  2007        PMID: 17557421

Source DB:  PubMed          Journal:  J Clin Sleep Med        ISSN: 1550-9389            Impact factor:   4.062


  22 in total

1.  Time frequency analysis for automated sleep stage identification in fullterm and preterm neonates.

Authors:  Luay Fraiwan; Khaldon Lweesy; Natheer Khasawneh; Mohammad Fraiwan; Heinrich Wenz; Hartmut Dickhaus
Journal:  J Med Syst       Date:  2009-12-10       Impact factor: 4.460

2.  Sleep scoring: man vs. machine?

Authors:  Christian Berthomier; Marie Brandewinder
Journal:  Sleep Breath       Date:  2012-05-06       Impact factor: 2.816

Review 3.  Rethinking sleep analysis.

Authors:  Hartmut Schulz
Journal:  J Clin Sleep Med       Date:  2008-04-15       Impact factor: 4.062

4.  Fully parametric sleep staging compatible with the classical criteria.

Authors:  Urszula Malinowska; Hubert Klekowicz; Andrzej Wakarow; Szymon Niemcewicz; Piotr J Durka
Journal:  Neuroinformatics       Date:  2009-12

5.  Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System.

Authors:  Naresh M Punjabi; Naima Shifa; Georg Dorffner; Susheel Patil; Grace Pien; Rashmi N Aurora
Journal:  Sleep       Date:  2015-10-01       Impact factor: 5.849

Review 6.  The future of sleep health: a data-driven revolution in sleep science and medicine.

Authors:  Ignacio Perez-Pozuelo; Bing Zhai; Joao Palotti; Raghvendra Mall; Michaël Aupetit; Juan M Garcia-Gomez; Shahrad Taheri; Yu Guan; Luis Fernandez-Luque
Journal:  NPJ Digit Med       Date:  2020-03-23

Review 7.  Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity.

Authors:  Diego R Mazzotti; Diane C Lim; Kate Sutherland; Lia Bittencourt; Jesse W Mindel; Ulysses Magalang; Allan I Pack; Philip de Chazal; Thomas Penzel
Journal:  Physiol Meas       Date:  2018-09-13       Impact factor: 2.833

Review 8.  Approaches to the assessment of arousals and sleep disturbance in children.

Authors:  Shalini Paruthi; Ronald D Chervin
Journal:  Sleep Med       Date:  2010-08       Impact factor: 3.492

9.  Performance of an automated polysomnography scoring system versus computer-assisted manual scoring.

Authors:  Atul Malhotra; Magdy Younes; Samuel T Kuna; Ruth Benca; Clete A Kushida; James Walsh; Alexandra Hanlon; Bethany Staley; Allan I Pack; Grace W Pien
Journal:  Sleep       Date:  2013-04-01       Impact factor: 5.849

10.  The use of combined thermal/pressure polyvinylidene fluoride film airflow sensor in polysomnography.

Authors:  Meir Kryger; Todd Eiken; Li Qin
Journal:  Sleep Breath       Date:  2013-05-29       Impact factor: 2.816

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