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.
Highlights
Cardiovascular diseases (CVDs) are a major threat to human health
Since energy consumption is proportional to clock frequency [9], the node works at high-speed operation mode (HSOM) with the master clock during tasks of data compression and transmission and at low-speed operation mode (LSOM) with the secondary clock in tasks of sampling
It is observed that the combination of block sparse Bayesian learning (BSBL) [20] and discrete cosine transform (DCT) basis achieves the highest signal recovery quality, and the percentage root-mean-square difference (PRD) are less than 3.6%
Summary
Cardiovascular diseases (CVDs) are a major threat to human health. According to the report of WTO, about 17.5 million people died of heart disease every year around the world [1]. With the rapid development of wireless body sensor networks (WBSNs) and wearable techniques, lots of WBSN-enabled ambulatory ECG monitoring devices have been developed [1, 5, 6] They could be seamlessly integrated into patients’ life for heart status monitoring, providing early warning to avoid accidental adverse cardiovascular events. Existing literature has shown that achieving truly energyefficient wireless ECG node requires ultra-lowpower devices and advanced communication protocols and proper data compression technologies. These existing studies explored power saving methods from different aspects, few of them gave out a whole energy-efficient node scheme.
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