Literature DB >> 19936970

Fully parametric sleep staging compatible with the classical criteria.

Urszula Malinowska1, Hubert Klekowicz, Andrzej Wakarow, Szymon Niemcewicz, Piotr J Durka.   

Abstract

We present an open system for sleep staging, based explicitly on the criteria used in visual EEG analysis. Slow waves, theta and alpha waves, sleep spindles and K-complexes are parameterized in terms of time duration, amplitude, and frequency of each waveform by means of the matching pursuit algorithm. It allows the detection of these structures using mostly the criteria from visual EEG analysis. For example, within this framework for the first time we compute directly the relative duration of slow waves, which is a basic parameter in recognition of deep sleep stages. Performance of the system is evaluated on 20 polysomnographic recordings, scored by experienced encephalographers. Seven recordings were scored by more than one expert. Proposed system gives concordance with visual staging close to the inter-expert concordance. The algorithm is implemented in a user-friendly software system for display and analysis of polysomnographic recordings, freely available with complete source code from http://signalml.org/svarog.html .

Mesh:

Year:  2009        PMID: 19936970     DOI: 10.1007/s12021-009-9059-9

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  21 in total

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3.  High resolution parametric description of slow wave sleep.

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4.  Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients.

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Journal:  Sleep       Date:  1996-01       Impact factor: 5.849

5.  On the use of neural network techniques to analyze sleep EEG data. Third communication: robustification of the classificator by applying an algorithm obtained from 9 different networks.

Authors:  R Baumgart-Schmitt; W M Herrmann; R Eilers
Journal:  Neuropsychobiology       Date:  1998       Impact factor: 2.328

6.  EEG sleep stage scoring by an automatic hybrid system.

Authors:  J R Smith; I Karacan
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1971-09

7.  An E-health solution for automatic sleep classification according to Rechtschaffen and Kales: validation study of the Somnolyzer 24 x 7 utilizing the Siesta database.

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Journal:  Neuropsychobiology       Date:  2005-04-18       Impact factor: 2.328

8.  AI-based approach to automatic sleep classification.

Authors:  M Kubat; G Pfurtscheller; D Flotzinger
Journal:  Biol Cybern       Date:  1994       Impact factor: 2.086

9.  C STAGE, automated sleep scoring: development and comparison with human sleep scoring for healthy older men and women.

Authors:  P N Prinz; L H Larsen; K E Moe; E M Dulberg; M V Vitiello
Journal:  Sleep       Date:  1994-12       Impact factor: 5.849

10.  Automated sleep scoring: a comparative reliability study of two algorithms.

Authors:  E Stanus; B Lacroix; M Kerkhofs; J Mendlewicz
Journal:  Electroencephalogr Clin Neurophysiol       Date:  1987-04
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2.  Musical ratios in sounds from the human cochlea.

Authors:  Katarzyna J Blinowska; Konrad Kwaskiewicz; W Wiktor Jedrzejczak; Henryk Skarzynski
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3.  Spindles in Svarog: framework and software for parametrization of EEG transients.

Authors:  Piotr J Durka; Urszula Malinowska; Magdalena Zieleniewska; Christian O'Reilly; Piotr T Różański; Jarosław Żygierewicz
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4.  Multivariate matching pursuit in optimal Gabor dictionaries: theory and software with interface for EEG/MEG via Svarog.

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5.  Bilateral single-site intracerebral injection of a nonpathogenic herpes simplex virus-1 vector decreases anxiogenic behavior in MPS VII mice.

Authors:  Wenpei Liu; Gerald Griffin; Trena Clarke; Michael K Parente; Rita J Valentino; John H Wolfe; Nigel W Fraser
Journal:  Mol Ther Methods Clin Dev       Date:  2015-01-28       Impact factor: 6.698

6.  Electroencephalographic profiles for differentiation of disorders of consciousness.

Authors:  Urszula Malinowska; Camille Chatelle; Marie-Aurélie Bruno; Quentin Noirhomme; Steven Laureys; Piotr J Durka
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7.  Analysis of Multichannel EEG Patterns During Human Sleep: A Novel Approach.

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  7 in total

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