Jusak, Jusak, and Mahmoud, Seedahmed S. (2018) A novel and low processing time ECG security method suitable for sensor node platforms. International Journal of Communication Networks and Information Security, 10 (1). 24. pp. 213-222.

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Abstract

An anonymisation of electrocardiogram (ECG) signal is essential during the distribution and storage in a public repository. In this paper, we introduce a novel low processing time ECG anonymisation method employing the fast Fourier transform (FFT) method. The proposed framework is suitable for sensor node platforms due to its low processing time. It was developed to address two major inherent limitations in the Internet of Medical Thing (IMedT) environment including most current requirement for securing ECG signal and urgent need for efficient methods to overcome physical limitation of sensor nodes. Ramifications from computer simulation showed that the proposed model was able to obscure both fiducial and non-fiducial features of the ECG signals. Performance evaluation between the original and the reconstructed ECG signals revealed strong cross-correlation implying lossless reconstruction of the original ECG signal. Furthermore, the proposed method achieved a lower processing time security algorithm as compared with the recently proposed wavelet based anonymisation methods. Finally, the proposed framework offered advantages in terms of flexibility in determining the secret key length makes it suitable for various applications.

Item ID:	68866
Item Type:	Article (Research - C1)
ISSN:	2073-607X
Keywords:	anonymisation, electrocardiogram, fast Fourier transform, internet of medical things, internet of things
Copyright Information:	© IJCNIS
Date Deposited:	02 Aug 2021 22:19
FoR Codes:	40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460403 Data security and protection @ 50%
SEO Codes:	20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified @ 100%
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