ECG feature extraction without fiducial detection:  Applications to ECG biometric recognition.
Published: 2006
Language: eng
Number of pages: 71
ISBN-13: 9780494211335
Description: Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric, the human heartbeat, can be used for identity recognition. Existing approaches address the problem by using electrocardiogram (ECG) data and the fiducials of the different parts of the pulses in the data. However, the current fiducial detection tools are inadequate for this application since the boundaries of waveforms are hard to detect and also there is no universal rule to define where exactly they are located. In this thesis, an ECG biometric recognition method that does not require any pulse annotations is introduced based on classification of coefficients from the Discrete Cosine Transform (DCT) of the Autocorrelation (AC) sequence of ECG data segments. Low false negative and false positive rates and a 100% subject recognition rate for healthy subjects can be achieved with parameters that are suitable for the database.
Notes: Source: Masters Abstracts International, Volume: 45-03, page: 1604.

Advisor: Dimitrios Hatzinakos.

Thesis (M.A.Sc.)--University of Toronto, 2006.

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