Signatures of Depression in Non-Stationary Biometric Time Series

Author:

Culic Milka1,Gjoneska Biljana2,Hinrikus Hiie3,Jändel Magnus4,Klonowski Wlodzimierz5,Liljenström Hans6,Pop-Jordanova Nada7,Psatta Dan8,von Rosen Dietrich6,Wahlund Björn9

Affiliation:

1. Department of Neurobiology Institute for Biological Research, University of Belgrade, 11000 Belgrade, Serbia

2. Division of Bioinformatics, Macedonian Academy of Sciences and Arts, 1000 Skopje, Macedonia

3. Department of Biomedical Engineering, Technomedicum of the Tallinn University of Technology, 19086 Tallinn, Estonia

4. The Swedish Defence Research Agency, SE-16490 Stockholm, Sweden

5. Lab of Biosignal Analysis Fundamentals, Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, 00901 Warsaw, Poland

6. Department of Energy and Technology, Swedish University of Agricultural Sciences, SE-75007 Uppsala, Sweden

7. Pediatric Clinic, Faculty of Medicine, University of Skopje, 1000 Skopje, Macedonia

8. Institute of Neurology and Psychiatry in Bucharest, 75622 Bucharest, Romania

9. Department of Clinical Neuroscience, Division of Psychiatry, Karolinska Instititute, SE-17177 Stockholm, Sweden

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.

Funder

European Cooperation in Science and Technology

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

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