A Continuous Biomedical Signal Acquisition System Based on Compressed Sensing in Body Sensor Networks

2013 ◽  
Vol 9 (3) ◽  
pp. 1764-1771 ◽  
Author(s):  
Shancang Li ◽  
Li Da Xu ◽  
Xinheng Wang
Keyword(s):  
Sensor Networks ◽  
Compressed Sensing ◽  
Acquisition System ◽  
Body Sensor Networks ◽  
Signal Acquisition ◽  
Biomedical Signal

A Wireless Biomedical Signal Interface System-on-Chip for Body Sensor Networks

2010 ◽  
Vol 4 (2) ◽  
pp. 112-117 ◽  
Author(s):  
Lei Wang ◽  
Guang-Zhong Yang ◽  
Jin Huang ◽  
Jinyong Zhang ◽  
Li Yu ◽  
...  
Keyword(s):  
Sensor Networks ◽  
System On Chip ◽  
Body Sensor Networks ◽  
Biomedical Signal ◽  
Interface System ◽  
On Chip ◽  
Signal Interface

scholarly journals Comparative Study of Compressed Sensing for Heart Sound Acquisition in Wireless Body Sensor Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22483-22492
Author(s):  
Shuang Sun ◽  
Jiazhu Xing ◽  
Zheng Zhou ◽  
Wei Wang ◽  
Junxin Chen
Keyword(s):  
Sensor Networks ◽  
Compressed Sensing ◽  
Comparative Study ◽  
Heart Sound ◽  
Body Sensor Networks ◽  
Wireless Body Sensor Networks

Compressed Sensing Based Neural Signal Processing and Performance Analysis

2014 ◽  
Vol 513-517 ◽  
pp. 1595-1599
Author(s):  
Yu Xi Zhang ◽  
Wen Gui Fan ◽  
Jin Ping Sun
Keyword(s):  
Signal Processing ◽  
Compressed Sensing ◽  
Power Consumption ◽  
Digital Signal ◽  
Data Rate ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Signal Compression ◽  
Sensing Matrix ◽  
Neural Signal

Measurement of neural signal provides important value for study of brain function and the pathogenesis of neurological. With emerging extensive research of electrical activity, more and more neural signal need to be collected, transmitted and stored, making the compression processing of neural signal become important part of digital signal processing. In recent years, ASIC-based wireless neural signal acquisition system has been developed rapidly, encountered strict restrictions on power consumption which is dominant determined by the data rate and complexity of algorithm. In order to reduce power consumption, lower data rate and algorithm with lower complexity needed to be selected when design a neural acquisition system. This paper focus on neural signal compression method based on compressed sensing and its performance and compare it with conventional compression algorithm. We compare complexity of various algorithms in the view of circuit complement, show that the complexity of neural signal compression can be dramatically reduced by using specially designed compressed sensing matrix, thereby reducing the system power consumption.

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