Authors:
Tiago Araújo
1
;
Neuza Nunes
2
;
Hugo Gamboa
3
and
Ana Fred
4
Affiliations:
1
New University of Lisbon and Plux Wireless Biosignals, Portugal
;
2
Plux Wireless Biosignals, Portugal
;
3
New University of Lisbon, Portugal
;
4
Instituto de Telecomunicações and Instituto Superior Técnico, Portugal
Keyword(s):
Biometry, Classification, Electrocardiography, Meanwave, Signal Processing.
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%. Th
e 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.
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