Signatures of depression in non-stationary biometric time series

  • Authors:

  • Milka Culic;Biljana Gjoneska;Hiie Hinrikus;Magnus Jändel;Wlodzimierz Klonowski;Hans Liljenström;Nada Pop-Jordanova;Dan Psatta;Dietrich Von Rosen;Björn Wahlund

  • Affiliations:

  • Department of Neurobiology Institute for Biological Research, University of Belgrade, Belgrade, Serbia;Division of Bioinformatics, Macedonian Academy of Sciences and Arts, Skopje, Macedonia;Department of Biomedical Engineering, Technomedicum of the Tallinn University of Technology, Tallinn, Estonia;The Swedish Defence Research Agency, Stockholm, Sweden;Lab of Biosignal Analysis Fundamentals, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland;Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden;Pediatric Clinic, Faculty of Medicine, University of Skopje, Skopje, Macedonia;Institute of Neurology and Psychiatry in Bucharest, Bucharest, Romania;Department of Energy and Technology, Swedish University of Agricultural Sciences, Uppsala, Sweden;Department of Clinical Neuroscience, Karolinska Instititute, Stockholm, Sweden

  • Venue:
  • Computational Intelligence and Neuroscience - Neuromath: advanced methods for the estimation of human brain activity and connectivity
  • Year:
  • 2009

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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.