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Signatures of Depression in Non-Stationary Biometric Time Series

This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed op...

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Autores principales: Culic, Milka, Gjoneska, Biljana, Hinrikus, Hiie, Jändel, Magnus, Klonowski, Wlodzimierz, Liljenström, Hans, Pop-Jordanova, Nada, Psatta, Dan, von Rosen, Dietrich, Wahlund, Björn
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703133/
https://www.ncbi.nlm.nih.gov/pubmed/19584928
http://dx.doi.org/10.1155/2009/989824
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author Culic, Milka
Gjoneska, Biljana
Hinrikus, Hiie
Jändel, Magnus
Klonowski, Wlodzimierz
Liljenström, Hans
Pop-Jordanova, Nada
Psatta, Dan
von Rosen, Dietrich
Wahlund, Björn
author_facet Culic, Milka
Gjoneska, Biljana
Hinrikus, Hiie
Jändel, Magnus
Klonowski, Wlodzimierz
Liljenström, Hans
Pop-Jordanova, Nada
Psatta, Dan
von Rosen, Dietrich
Wahlund, Björn
author_sort Culic, Milka
collection PubMed
description This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented.
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spelling pubmed-27031332009-07-07 Signatures of Depression in Non-Stationary Biometric Time Series Culic, Milka Gjoneska, Biljana Hinrikus, Hiie Jändel, Magnus Klonowski, Wlodzimierz Liljenström, Hans Pop-Jordanova, Nada Psatta, Dan von Rosen, Dietrich Wahlund, Björn Comput Intell Neurosci Review Article This paper is based on a discussion that was held during a special session on models of mental disorders, at the NeuroMath meeting in Stockholm, Sweden, in September 2008. At this occasion, scientists from different countries and different fields of research presented their research and discussed open questions with regard to analyses and models of mental disorders, in particular depression. The content of this paper emerged from these discussions and in the presentation we briefly link biomarkers (hormones), bio-signals (EEG) and biomaps (brain-maps via EEG) to depression and its treatments, via linear statistical models as well as nonlinear dynamic models. Some examples involving EEG-data are presented. Hindawi Publishing Corporation 2009 2009-06-24 /pmc/articles/PMC2703133/ /pubmed/19584928 http://dx.doi.org/10.1155/2009/989824 Text en Copyright © 2009 Milka Culic et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Culic, Milka
Gjoneska, Biljana
Hinrikus, Hiie
Jändel, Magnus
Klonowski, Wlodzimierz
Liljenström, Hans
Pop-Jordanova, Nada
Psatta, Dan
von Rosen, Dietrich
Wahlund, Björn
Signatures of Depression in Non-Stationary Biometric Time Series
title Signatures of Depression in Non-Stationary Biometric Time Series
title_full Signatures of Depression in Non-Stationary Biometric Time Series
title_fullStr Signatures of Depression in Non-Stationary Biometric Time Series
title_full_unstemmed Signatures of Depression in Non-Stationary Biometric Time Series
title_short Signatures of Depression in Non-Stationary Biometric Time Series
title_sort signatures of depression in non-stationary biometric time series
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2703133/
https://www.ncbi.nlm.nih.gov/pubmed/19584928
http://dx.doi.org/10.1155/2009/989824
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