Low-Cost and Confidential ECG Acquisition Framework Using Compressed Sensing and Chaotic Systems for Wireless Body Area NetworkShow others and affiliations
2022 (English)In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 26, no 12, p. 5783-5792Article in journal (Refereed) Published
Abstract [en]
Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this paper, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order <inline-formula><tex-math notation="LaTeX">$\Sigma \Delta$</tex-math></inline-formula> quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.
Place, publisher, year, edition, pages
IEEE, 2022. Vol. 26, no 12, p. 5783-5792
Keywords [en]
Body area networks, Chaotic Encryption, Compressed Sensing, Electrical resistance measurement, Electrocardiogram, Electrocardiography, Encryption, Sensors, Wireless Body Area Network, Wireless communication, Costs, Cryptography, Energy utilization, Signal reconstruction, Wireless local area networks (WLAN), Bodyarea networks (BAN), Chaotic encryptions, Compressed-Sensing, CryptoGraphics, Low-costs, Physiological signals, Wireless communications, Electrocardiograms
National Category
Computer Sciences Computer Engineering Computer Systems
Identifiers
URN: urn:nbn:se:uu:diva-491993DOI: 10.1109/JBHI.2022.3206232ISI: 000894943300004Scopus ID: 2-s2.0-85139413937OAI: oai:DiVA.org:uu-491993DiVA, id: diva2:1722539
Note
Export Date: 29 December 2022; Article; CODEN: ITIBF
2022-12-292022-12-292023-01-13Bibliographically approved