Literature DB >> 18340270

The colorful brain: visualization of EEG background patterns.

Michel J A M van Putten1.   

Abstract

This article presents a method to transform routine clinical EEG recordings to an alternative visual domain. The method is intended to support the classic visual interpretation of the EEG background pattern and to facilitate communication about relevant EEG characteristics. In addition, it provides various quantitative features. The EEG features used in the transformation include color-coded time-frequency representations of two novel symmetry measures and a synchronization measure, based on a nearest-neighbor coherence estimate. This triplet captures three highly relevant aspects of the dynamics of the EEG background pattern, which correlate strongly with various neurologic conditions. In particular, it quantifies and visualizes the spatiotemporal distribution of the EEG power in the anterioposterior and lateral direction, and the short-distance coherence. The potential clinical use is illustrated by application of the proposed technique to various normal and abnormal EEGs, including seizure activity and the transition to sleep. The proposed transformation visualizes various essential elements of EEG background patterns. Quantitative analysis of clinical EEG recordings and transformation to alternative domains assists in the interpretation and contributes to an objective interpretation.

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Year:  2008        PMID: 18340270     DOI: 10.1097/WNP.0b013e31816bdf85

Source DB:  PubMed          Journal:  J Clin Neurophysiol        ISSN: 0736-0258            Impact factor:   2.177


  6 in total

1.  Guiding Principles for a Pediatric Neurology ICU (neuroPICU) Bedside Multimodal Monitor: Findings from an International Working Group.

Authors:  Zachary M Grinspan; Yonina C Eldar; Daniel Gopher; Amihai Gottlieb; Rotem Lammfromm; Halinder S Mangat; Nimrod Peleg; Steven Pon; Igal Rozenberg; Nicholas D Schiff; David E Stark; Peter Yan; Hillel Pratt; Barry E Kosofsky
Journal:  Appl Clin Inform       Date:  2016-05-18       Impact factor: 2.342

2.  Quantification of EEG reactivity in comatose patients.

Authors:  Mathilde C Hermans; M Brandon Westover; Michel J A M van Putten; Lawrence J Hirsch; Nicolas Gaspard
Journal:  Clin Neurophysiol       Date:  2015-07-02       Impact factor: 3.708

3.  Predicting sex from brain rhythms with deep learning.

Authors:  Michel J A M van Putten; Sebastian Olbrich; Martijn Arns
Journal:  Sci Rep       Date:  2018-02-15       Impact factor: 4.379

4.  Computer-assisted interpretation of the EEG background pattern: a clinical evaluation.

Authors:  Shaun S Lodder; Jessica Askamp; Michel J A M van Putten
Journal:  PLoS One       Date:  2014-01-24       Impact factor: 3.240

5.  A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest.

Authors:  Marleen C Tjepkema-Cloostermans; Fokke B van Meulen; Gjerrit Meinsma; Michel J A M van Putten
Journal:  Crit Care       Date:  2013-10-22       Impact factor: 9.097

6.  Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography.

Authors:  Marjolein E Haveman; Michel J A M Van Putten; Harold W Hom; Carin J Eertman-Meyer; Albertus Beishuizen; Marleen C Tjepkema-Cloostermans
Journal:  Crit Care       Date:  2019-12-11       Impact factor: 9.097

  6 in total

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