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Computational Intelligence and Neuroscience
Volume 2009 (2009), Article ID 989824, 7 pages
http://dx.doi.org/10.1155/2009/989824
Review Article

Signatures of Depression in Non-Stationary Biometric Time Series

1Department of Neurobiology Institute for Biological Research, University of Belgrade, 11000 Belgrade, Serbia
2Division of Bioinformatics, Macedonian Academy of Sciences and Arts, 1000 Skopje, Macedonia
3Department of Biomedical Engineering, Technomedicum of the Tallinn University of Technology, 19086 Tallinn, Estonia
4The Swedish Defence Research Agency, SE-16490 Stockholm, Sweden
5Lab of Biosignal Analysis Fundamentals, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 00901 Warsaw, Poland
6Department of Energy and Technology, Swedish University of Agricultural Sciences, SE-75007 Uppsala, Sweden
7Pediatric Clinic, Faculty of Medicine, University of Skopje, 1000 Skopje, Macedonia
8Institute of Neurology and Psychiatry in Bucharest, 75622 Bucharest, Romania
9Department of Clinical Neuroscience, Division of Psychiatry, Karolinska Instititute, SE-17177 Stockholm, Sweden

Received 30 October 2008; Revised 19 March 2009; Accepted 29 April 2009

Academic Editor: Laura Astolfi

Copyright © 2009 Milka Culic et al. 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.

Abstract

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.