NDLI: ECG biometric recognition in different physiological conditions using robust normalized QRS complexes

Content Provider	IEEE Xplore Digital Library
Author	Sidek, K.A. Khalil, I. Smolen, M.
Copyright Year	2012
Description	Author affiliation: RMIT University, Melbourne, Australia (Sidek, K.A.; Khalil, I.) \|\| AGH University of Science and Technology, Krakow, Poland (Smolen, M.)
Abstract	This paper demonstrates subject recognition using electrocardiogram (ECG) signal in different physiological conditions. A total of 30 subjects used in this study were obtained from a non-invasive measurement called the Revitus ECG module. Each subject performed six physiological activities which are walking, going upstairs, going downstairs, natural gait, lying with position changed and resting while watching TV. Unique features were extracted in these different physiological conditions from the same subject using normalized QRS complex technique. One physiological activity acts as the enrolment template while the remaining five activities represent the recognition data. Cross correlation was used to measure the similarity between activities. Later, Multilayer Perceptron classifier was applied to evaluate the distinctiveness between subjects. The results of the experiment show that QRS complexes in different activities from the same subject were strongly correlated to each other by obtaining correlation values of more than 0.9. A classification accuracy of 96.1% when using the proposed normalized method as compared to 93.4% without using the normalized QRS complex proves to distinguish between subjects.
Starting Page	97
Ending Page	100
File Size	239995
Page Count	4
File Format	PDF
ISBN	9781467320764
ISSN	2325887X
e-ISBN	9781467320771
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-09
Publisher Place	Poland
Access Restriction	Subscribed
Rights Holder	Creative Commons Attribution License 2.5 (CCAL)
Subject Keyword	Electrocardiography Physiology Feature extraction Correlation Multilayer perceptrons Robustness Educational institutions
Content Type	Text
Resource Type	Article
Subject	Computer Science Cardiology and Cardiovascular Medicine

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