1. A Digital Compressed Sensing-Based Energy-Efficient Single-Spot Bluetooth ECG Node
- Author
-
Du Keqin, Jianqing Li, Xiangyu Zhang, Zhipeng Cai, Fumin Zou, and Kan Luo
- Subjects
lcsh:Medical technology ,Article Subject ,Computer science ,0206 medical engineering ,Real-time computing ,Biomedical Engineering ,Health Informatics ,02 engineering and technology ,law.invention ,Bluetooth ,law ,0202 electrical engineering, electronic engineering, information engineering ,Discrete cosine transform ,Wireless ,Humans ,lcsh:R5-920 ,business.industry ,Node (networking) ,020206 networking & telecommunications ,Signal Processing, Computer-Assisted ,Energy consumption ,Equipment Design ,020601 biomedical engineering ,Compressed sensing ,lcsh:R855-855.5 ,Electrocardiography, Ambulatory ,Surgery ,business ,lcsh:Medicine (General) ,Wireless Technology ,Algorithms ,Biotechnology ,Efficient energy use ,Data compression ,Research Article - Abstract
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.
- Published
- 2018