%0 Journal Article
%T Signatures of Depression in Non-Stationary Biometric Time Series
%A Milka Culic
%A Biljana Gjoneska
%A Hiie Hinrikus
%A Magnus J ndel
%A Wlodzimierz Klonowski
%A Hans Liljenstr m
%A Nada Pop-Jordanova
%A Dan Psatta
%A Dietrich von Rosen
%A Bj rn Wahlund
%J Computational Intelligence and Neuroscience
%D 2009
%I Hindawi Publishing Corporation
%R 10.1155/2009/989824
%X 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.
%U http://www.hindawi.com/journals/cin/2009/989824/