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Distance-based Algorithm for Biometric Applications in Meanwaves of Subject’s Heartbeats

Araújo, T. ; Nunes, N. ; Gamboa, H. ; Fred, A. L. N.

Distance-based Algorithm for Biometric Applications in Meanwaves of Subject’s Heartbeats, Proc International Conf. on Pattern Recognition Applications and Methods - ICPRAM, Barcelona, Spain, Vol. 1, pp. 630 - 634, February, 2013.

Digital Object Identifier: 10.5220/0004358106300634

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Abstract
The authors present a new biometric classification procedure based on meanwave’s distances of electrocardiogram (ECG) heartbeats. The ECG data was collected from 63 subjects during two data-recording sessions separated by six months (Time Instance 1, T1, and Time Instance 2, T2). Two classification tests were performed with the goal of subject identification using a distance-based method with the heartbeat waves. In both tests, the enrollment template was composed by the averaging of the T1 waves for each subject. For the first test, we composed five meanwaves of different T1 waves; In the second test, five meanwaves of different groups of T2 waves were composed. Classification was performed through the implementation of a kNN classifier, using the meanwave’s Euclidean distances as features for subject identification. In the first test, with only T1 waves, 95.2% of accuracy was achieved. In the second test, using T2 waves to compose the dataset for testing, the accuracy was 90.5%. The T2 waves belonged to the same subjects but were acquired in different time instances, simulating a real biometric identification problem. We therefore conclude that a distance-based method using meanwaves of ECG heartbeats for each subject is a valid parameter for classification in biometric applications.