title: Analysis of complex physiological systems by information flow: a time scale-specific complexity assessment.
abstract: In the last two decades conventional linear methods for biosignal analysis have been substantially extended by non-stationary, non-linear, and complexity approaches. So far, complexity is usually assessed with regard to one single time scale, disregarding complex physiology organised on different time scales. This shortcoming was overcome and medically evaluated by information flow functions developed in our research group in collaboration with several theoretical, experimental, and clinical partners. In the present work, the information flow is introduced and typical information flow characteristics are demonstrated. The prognostic value of autonomic information flow (AIF), which reflects communication in the cardiovascular system, was shown in patients with multiple organ dysfunction syndrome and in patients with heart failure. Gait information flow (GIF), which reflects communication in the motor control system during walking, was introduced to discriminate between controls and elderly patients suffering from low back pain. The applications presented for the theoretically based approach of information flow confirm its value for the identification of complex physiological systems. The medical relevance has to be confirmed by comprehensive clinical studies. These information flow measures substantially extend the established linear and complexity measures in biosignal analysis.
authors.affiliation: Systems Analysis Research Group, Biomagnetic Centre, Department of Neurology, Friedrich Schiller University, Jena, Germany. dirk.hoyer@biomag.uni-jena.de
authors.forename: Dirk
authors.initials: D
authors.lastname: Hoyer
authors.forename: Birgit
authors.initials: B
authors.lastname: Frank
authors.forename: Bernd
authors.initials: B
authors.lastname: Pompe
authors.forename: Hendrik
authors.initials: H
authors.lastname: Schmidt
authors.forename: Karl
authors.initials: K
authors.lastname: Werdan
authors.forename: Ursula
authors.initials: U
authors.lastname: Müller-Werdan
authors.forename: Rafal
authors.initials: R
authors.lastname: Baranowski
authors.forename: Jan J
authors.initials: JJ
authors.lastname: Zebrowski
authors.forename: Winfried
authors.initials: W
authors.lastname: Meissner
authors.forename: Ulf
authors.initials: U
authors.lastname: Kletzin
authors.forename: Daniela
authors.initials: D
authors.lastname: Adler
authors.forename: Steffen
authors.initials: S
authors.lastname: Adler
authors.forename: Reinhard
authors.initials: R
authors.lastname: Blickhan
mesh_terms: D000328:Adult; D000465:Algorithms; D001341:Autonomic Nervous System / Q000503:physiopathology*; D001416:Back Pain / Q000175:diagnosis* / Q000503:physiopathology; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000503:physiopathology; D019295:Computational Biology / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D005684:Gait; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D009102:Multiple Organ Failure / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated / Q000379:methods; D013997:Time Factors