Abstract
The paper proposed an Electrocardiogram (ECG) feature extraction method for biometric. It relied on ECG superposition number matrix built by several single heartbeat ECG data. The target of the study was to find stable features of the ECG signal under unrestricted status for biometric. By matrix segmentation and similarity comparison, the stable feature distribution was gotten, and stable feature sets were also constructed. 13 volunteers’ ECG data collected by self-made ECG device in different status were gotten, the collecting period was lasting for half year; 28 healthy individuals’ ECG data under calm status were also collected; Besides that, 14 subjects’ ECG data in MIT-BIH were also involved in study. From the result of experiments, the average True Positive Rate (TPR) reached 83.21%, 83.93% and 80% on MIT data set, ECG data set in calm status and ECG data in different status respectively. It is also found that along with the increasing amount of ECG single heartbeat used to build superposition matrix, the stable features of one’s ECG were gradually revealed and this helped ECG based biometric effectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ghazi, M.M., Ekenel, H.K.: A comprehensive analysis of deep learning based representation for face recognition. In: Computer Vision and Pattern Recognition Workshops, pp. 102–109 (2016)
Ali, M.M.H., Mahale, V.H., Yannawar, P., Gaikwad, A.T.: Fingerprint recognition for person identification and verification based on minutiae matching. IEEE International Conference on Advanced Computing, pp. 332–339 (2016)
Garagad, V.G., Iyer, N.C.: A novel technique of iris identification for biometric systems. In: International Conference on Advances in Computing, pp. 973–978 (2014)
Ramos, J., Ausín, J.L., Lorido, A.M., Redondo, F., Duque-Carrillo, J.F.: A wireless multi-channel bioimpedance measurement system for personalized healthcare and lifestyle. Stud. Health Technol. Inf. 189, 59–67 (2013)
Odinaka, I., Lai, P.H., Kaplan, A.D., O’Sullivan, J.A., Sirevaag, E.J.: ECG biometric recognition: a comparative analysis. IEEE Trans. Inf. Forensics Secur. 7, 1812–1814 (2012)
Singh, Y.N.: Human recognition using Fisher’s discriminant analysis of heartbeat interval features and ECG morphology. Neurocomputing 167, 322–335 (2015)
Hamdi, T., Ben Slimane, A., Ben Khalifa, A.: A novel feature extraction method in ECG biometrics. In: Image Processing, Applications and Systems Conference, pp. 1–5 (2014)
Paulet, M.V., Salceanu, A., Salceanu, A.: Automatic recognition of the person by ECG signals characteristics. In: International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp. 281–284 (2015)
Choi, H.S., Lee, B., Yoon, S.: Biometric authentication using noisy electrocardiograms acquired by mobile sensors. IEEE Access 4, 1266–1273 (2016)
Zhang, Y., Shi, Y.: A new method for ECG biometric recognition using a hierarchical scheme classifier. In: IEEE International Conference on Software Engineering and Service Science, pp. 457–460 (2015)
Tantawi, M.M., Revett, K., Salem, A.B., Tolba, M.F.: A wavelet feature extraction method for electrocardiogram (ECG)-based biometric recognition. Sig. Image Video Process. 9, 1271–1280 (2015)
Page, A., Kulkarni, A., Mohsenin, T.: Utilizing deep neural nets for an embedded ECG-based biometric authentication system. In: Biomedical Circuits and Systems Conference, pp. 1–4 (2015)
Jahiruzzaman, M., Hossain, A.B.M.A.: ECG based biometric human identification using chaotic encryption. In: International Conference on Electrical Engineering and Information Communication Technology (2015)
Zheng, G., Chen, Y., Dai, M.: HRV based stress recognizing by random forest. Fuzzy Syst. Data Min. II, 444–458 (2016)
Acknowledgment
The paper is supported by TianJin National Science Foundation 16JCYBJC15300 (2016.04-2019.03).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, G., Sun, X., Ji, S., Dai, M., Sun, Y. (2018). ECG Based Biometric by Superposition Matrix in Unrestricted Status. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_59
Download citation
DOI: https://doi.org/10.1007/978-3-319-97909-0_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97908-3
Online ISBN: 978-3-319-97909-0
eBook Packages: Computer ScienceComputer Science (R0)