Literature DB >> 22030383

Sleep scoring using artificial neural networks.

Marina Ronzhina1, Oto Janoušek, Jana Kolářová, Marie Nováková, Petr Honzík, Ivo Provazník.   

Abstract

Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved - next to other classification methods - by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.
Copyright © 2011 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 22030383     DOI: 10.1016/j.smrv.2011.06.003

Source DB:  PubMed          Journal:  Sleep Med Rev        ISSN: 1087-0792            Impact factor:   11.609


  16 in total

1.  Single-channel EEG sleep stage classification based on a streamlined set of statistical features in wavelet domain.

Authors:  Thiago L T da Silveira; Alice J Kozakevicius; Cesar R Rodrigues
Journal:  Med Biol Eng Comput       Date:  2016-05-19       Impact factor: 2.602

2.  Odds ratio product of sleep EEG as a continuous measure of sleep state.

Authors:  Magdy Younes; Michele Ostrowski; Marc Soiferman; Henry Younes; Mark Younes; Jill Raneri; Patrick Hanly
Journal:  Sleep       Date:  2015-04-01       Impact factor: 5.849

3.  Sleep staging from single-channel EEG with multi-scale feature and contextual information.

Authors:  Kun Chen; Cheng Zhang; Jing Ma; Guangfa Wang; Jue Zhang
Journal:  Sleep Breath       Date:  2019-03-12       Impact factor: 2.816

4.  Automatic sleep stages classification using multi-level fusion.

Authors:  Hyungjik Kim; Seung Min Lee; Sunwoong Choi
Journal:  Biomed Eng Lett       Date:  2022-08-10

5.  Inter-hemispheric oscillations in human sleep.

Authors:  Lukas L Imbach; Esther Werth; Ulf Kallweit; Johannes Sarnthein; Thomas E Scammell; Christian R Baumann
Journal:  PLoS One       Date:  2012-11-07       Impact factor: 3.240

6.  A low computational cost algorithm for REM sleep detection using single channel EEG.

Authors:  Syed Anas Imtiaz; Esther Rodriguez-Villegas
Journal:  Ann Biomed Eng       Date:  2014-08-12       Impact factor: 3.934

7.  A Comparison Study on Multidomain EEG Features for Sleep Stage Classification.

Authors:  Yu Zhang; Bei Wang; Jin Jing; Jian Zhang; Junzhong Zou; Masatoshi Nakamura
Journal:  Comput Intell Neurosci       Date:  2017-11-05

8.  Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy.

Authors:  Jens B Stephansen; Alexander N Olesen; Mads Olsen; Aditya Ambati; Eileen B Leary; Hyatt E Moore; Oscar Carrillo; Ling Lin; Fang Han; Han Yan; Yun L Sun; Yves Dauvilliers; Sabine Scholz; Lucie Barateau; Birgit Hogl; Ambra Stefani; Seung Chul Hong; Tae Won Kim; Fabio Pizza; Giuseppe Plazzi; Stefano Vandi; Elena Antelmi; Dimitri Perrin; Samuel T Kuna; Paula K Schweitzer; Clete Kushida; Paul E Peppard; Helge B D Sorensen; Poul Jennum; Emmanuel Mignot
Journal:  Nat Commun       Date:  2018-12-06       Impact factor: 14.919

9.  Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders.

Authors:  Laure Peter-Derex; Christian Berthomier; Jacques Taillard; Pierre Berthomier; Romain Bouet; Jérémie Mattout; Marie Brandewinder; Hélène Bastuji
Journal:  J Clin Sleep Med       Date:  2021-03-01       Impact factor: 4.062

10.  An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems.

Authors:  Mesut Melek; Negin Manshouri; Temel Kayikcioglu
Journal:  Cogn Neurodyn       Date:  2020-10-12       Impact factor: 3.473

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