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.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/11131 |
| Date | 30 July 2008 |
| Creators | Agrafioti, Foteini |
| Contributors | Hatzinakos, Dimitrios |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
| Format | 5288176 bytes, application/pdf |
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