scholarly journals Robust Ultra-Low-Power Algorithm for Normal and Abnormal ECG Signals Based on Compressed Sensing Theory

2013 ◽  
Vol 19 ◽  
pp. 206-213 ◽  
Author(s):  
Mohammadreza Balouchestani ◽  
Kaamran Raahemifar ◽  
Sridhar Krishnan
Keyword(s):  
Compressed Sensing ◽  
Low Power ◽  
Ultra Low Power ◽  
Ecg Signals

scholarly journals Toward Ultra-Low-Power Remote Health Monitoring: An Optimal and Adaptive Compressed Sensing Framework for Activity Recognition

2019 ◽  
Vol 18 (3) ◽  
pp. 658-673 ◽  
Author(s):  
Josue Pagan ◽  
Ramin Fallahzadeh ◽  
Mahdi Pedram ◽  
Jose L. Risco-Martin ◽  
Jose M. Moya ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Low Power ◽  
Activity Recognition ◽  
Health Monitoring ◽  
Remote Health Monitoring ◽  
Ultra Low Power ◽  
Remote Health

An architecture for ultra-low-voltage ultra-low-power compressed sensing-based acquisition systems

2021 ◽  
Author(s):  
Carmine Paolino ◽  
Fabio Pareschi ◽  
Mauro Mangia ◽  
Riccardo Rovatti ◽  
Gianluca Setti
Keyword(s):  
Compressed Sensing ◽  
Low Power ◽  
Low Voltage ◽  
Ultra Low Power

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

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Kan Luo ◽  
Zhipeng Cai ◽  
Keqin Du ◽  
Fumin Zou ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Compressed Sensing ◽  
Bayesian Learning ◽  
Total Energy Consumption ◽  
Binary Matrix ◽  
Ultra Low Power ◽  
Single Spot ◽  
Ecg Signals ◽  
Analog Front End ◽  
Communication Module

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.

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