Literature DB >> 24459717

Semi-automatic sleep EEG scoring based on the hypnospectrogram.

Andreas M Koupparis, Vasileios Kokkinos, George K Kostopoulos.   

Abstract

BACKGROUND: Sleep EEG organization is revealed by sleep scoring, a time-consuming process based on strictly defined visual criteria. NEW
METHOD: We explore the possibility of sleep scoring using the whole-night time-frequency analysis, termed hypnospectrogram, with a computer-assisted K-means clustering method.
RESULTS: Hypnograms were derived from 10 whole-night sleep EEG recordings using either standard visual scoring under the Rechtshaffen and Kales criteria or semi-automated analysis of the hypnospectrogram derived from a single EEG electrode. We measured substantial agreement between the two approaches with Cohen's kappa considering all 7 stages at 0.61. COMPARISON WITH EXISTING
METHODS: A number of existing automated procedures have reached the level of human inter-rater agreement using the standard criteria. However, our approach offers the scorer the opportunity to exploit the information-rich graphic representation of the whole night sleep upon which the automated method works.
CONCLUSION: This work suggests that the hypnospectrogram can be used as an objective graphical rep-resentation of sleep architecture upon which sleep scoring can be performed with computer-assisted methods.

Entities:  

Mesh:

Year:  2014        PMID: 24459717

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  5 in total

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Authors:  Michael J Prerau; Ritchie E Brown; Matt T Bianchi; Jeffrey M Ellenbogen; Patrick L Purdon
Journal:  Physiology (Bethesda)       Date:  2017-01

2.  Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

Authors:  Farid Yaghouby; Sridhar Sunderam
Journal:  Comput Biol Med       Date:  2015-01-23       Impact factor: 4.589

3.  Co-activation of rhythms during alpha band oscillations as an interictal biomarker of exploding head syndrome.

Authors:  Dimitris Fotis Sakellariou; Alexander David Nesbitt; Sean Higgins; Sandor Beniczky; Jan Rosenzweig; Panagis Drakatos; Nadia Gildeh; Patrick Brian Murphy; Brian Kent; Adrian John Williams; Meir Kryger; Peter J Goadsby; Guy Doron Leschziner; Ivana Rosenzweig
Journal:  Cephalalgia       Date:  2020-04-10       Impact factor: 6.292

4.  Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times.

Authors:  Diego Alvarez-Estevez; Roselyne M Rijsman
Journal:  PLoS One       Date:  2022-09-29       Impact factor: 3.752

5.  Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.

Authors:  Julie A Onton; Dae Y Kang; Todd P Coleman
Journal:  Front Hum Neurosci       Date:  2016-11-29       Impact factor: 3.169

  5 in total

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