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  4. Directional information flow and applications
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Directional information flow and applications

Full metadata

Description

In the late 1960s, Granger published a seminal study on causality in time series, using linear interdependencies and information transfer. Recent developments in the field of information theory have introduced new methods to investigate the transfer of information in dynamical systems. Using concepts from Chaos and Markov theory, much of these methods have evolved to capture non-linear relations and information flow between coupled dynamical systems with applications to fields like biomedical signal processing. This thesis deals with the application of information theory to non-linear multivariate time series and develops measures of information flow to identify significant drivers and response (driven) components in networks of coupled sub-systems with variable coupling in strength and direction (uni- or bi-directional) for each connection. Transfer Entropy (TE) is used to quantify pairwise directional information. Four TE-based measures of information flow are proposed, namely TE Outflow (TEO), TE Inflow (TEI), TE Net flow (TEN), and Average TE flow (ATE). First, the reliability of the information flow measures on models, with and without noise, is evaluated. The driver and response sub-systems in these models are identified. Second, these measures are applied to electroencephalographic (EEG) data from two patients with focal epilepsy. The analysis showed dominant directions of information flow between brain sites and identified the epileptogenic focus as the system component typically with the highest value for the proposed measures (for example, ATE). Statistical tests between pre-seizure (preictal) and post-seizure (postictal) information flow also showed a breakage of the driving of the brain by the focus after seizure onset. The above findings shed light on the function of the epileptogenic focus and understanding of ictogenesis. It is expected that they will contribute to the diagnosis of epilepsy, for example by accurate identification of the epileptogenic focus from interictal periods, as well as the development of better seizure detection, prediction and control methods, for example by isolating pathologic areas of excessive information flow through electrical stimulation.

Date Created
2011
Contributors
  • Prasanna, Shashank (Author)
  • Jassemidis, Leonidas (Thesis advisor)
  • Tsakalis, Konstantinos (Thesis advisor)
  • Tepedelenlioğlu, Cihan (Committee member)
  • Arizona State University (Publisher)
Topical Subject
  • Electrical Engineering
  • Information Theory
  • Signal Processing
  • Time-series Analysis
  • Epilepsy--Computer simulation.
  • Epilepsy
Resource Type
Text
Genre
Masters Thesis
Academic theses
Extent
x, 75 p. : ill. (some col.)
Language
eng
Copyright Statement
In Copyright
Reuse Permissions
All Rights Reserved
Primary Member of
ASU Electronic Theses and Dissertations
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.9404
Statement of Responsibility
by Shashank Prasanna
Description Source
Viewed on Sept. 13, 2012
Level of coding
full
Note
Partial requirement for: M.S., Arizona State University, 2011
Note type
thesis
Includes bibliographical references (p. 72-75)
Note type
bibliography
Field of study: Electrical engineering
System Created
  • 2011-08-12 05:04:59
System Modified
  • 2021-08-30 01:51:28
  •     
  • 1 year 4 months ago
Additional Formats
  • OAI Dublin Core
  • MODS XML

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