Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2018 (2018), Article ID 2687389, 11 pages
Research Article

A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node

1Fujian Key Laboratory of Automotive Electronics and Electric Drive and School of Information Science and Engineering, Fujian University of Technology, Fuzhou 350118, China
2School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China

Correspondence should be addressed to Kan Luo

Received 31 August 2017; Accepted 5 December 2017; Published 11 January 2018

Academic Editor: Feng Liu

Copyright © 2018 Kan Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node’s specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.