T-Space at The University of Toronto Libraries >
School of Graduate Studies - Theses >
Please use this identifier to cite or link to this item:
|Title: ||Robust Subject Recognition Using the Electrocardiogram|
|Authors: ||Agrafioti, Foteini|
|Advisor: ||Hatzinakos, Dimitrios|
|Department: ||Electrical and Computer Engineering|
|Issue Date: ||30-Jul-2008|
|Abstract: ||This thesis studies the applicability of the electrocardiogram signal (ECG) as a biometric. There is strong evidence that heart's electrical activity embeds highly distinctive characteristics, suitable for applications such as the recognition of human subjects. Such systems traditionally provide two modes of functionality, identification and authentication; frameworks for subject recognition are herein proposed and analyzed in both scenarios.
As in most pattern recognition problems, the probability of mis-classification error decreases as more learning information becomes available. Thus, a central consideration is the design and evaluation of algorithms which exploit the added information provided by the 12 lead standard ECG recording system. Feature and decision level fusion techniques described in thesis, offer enhanced security levels.
The main novelty of the proposed approach, lies in the design of an identification system robust to cardiac arrhythmias. Criteria concerning the power distribution and information theoretic complexity of electrocardiogram windows are defined to signify abnormal ECG recordings, not suitable for recognition. Experimental results indicate high recognition rates and highlight identification based on ECG signals as very promising.|
|Appears in Collections:||Master|
The Edward S. Rogers Sr. Department of Electrical & Computer Engineering - Master theses
This item is licensed under a Creative Commons License
Items in T-Space are protected by copyright, with all rights reserved, unless otherwise indicated.