A Low-Offset and Low-Power CMOS Front-End Circuit for Physiological Signal Acquisition

Author(s):  
J. Y. Zhang ◽  
L. Wang ◽  
B. S. Liu ◽  
L. K. Chen ◽  
Y. T. Zhang ◽  
...  
2008 ◽  
Vol 2 (4) ◽  
pp. 280-288 ◽  
Author(s):  
Alex K. Y. Wong ◽  
Kong-Pang Pun ◽  
Yuan-Ting Zhang ◽  
Ka Nang Leung

Author(s):  
Jinyong Zhang ◽  
Lei Wang ◽  
Li Yu ◽  
Yabei Yang ◽  
Yuanting Zhang ◽  
...  

1998 ◽  
Vol 45 (4) ◽  
pp. 2272-2278 ◽  
Author(s):  
J. Vandenbussche ◽  
F. Leyn ◽  
G. Van der Plas ◽  
G. Gielen ◽  
W. Sansen

2018 ◽  
Vol 96 (1) ◽  
pp. 147-158 ◽  
Author(s):  
Priyesh P. Gandhi ◽  
N. M. Devashrayee

2018 ◽  
Vol 8 (3) ◽  
pp. 27 ◽  
Author(s):  
Avish Kosari ◽  
Jacob Breiholz ◽  
NingXi Liu ◽  
Benton Calhoun ◽  
David Wentzloff

This paper presents a power efficient analog front-end (AFE) for electrocardiogram (ECG) signal monitoring and arrhythmia diagnosis. The AFE uses low-noise and low-power circuit design methodologies and aggressive voltage scaling to satisfy both the low power consumption and low input-referred noise requirements of ECG signal acquisition systems. The AFE was realized with a three-stage fully differential AC-coupled amplifier, and it provides bio-signal acquisition with programmable gain and bandwidth. The AFE was implemented in a 130 nm CMOS process, and it has a measured tunable mid-band gain from 31 to 52 dB with tunable low-pass and high-pass corner frequencies. Under only 0.5 V supply voltage, it consumes 68 nW of power with an input-referred noise of 2.8 µVrms and a power efficiency factor (PEF) of 3.9, which makes it very suitable for energy-harvesting applications. The low-noise 68nW AFE was also integrated on a self-powered physiological monitoring System on Chip (SoC) that is used to capture ECG bio-signals. Heart rate extraction (R-R) detection algorithms were implemented and utilized to analyze the ECG data received by the AFE, showing the feasibility of <100 nW AFE for continuous ECG monitoring applications.


2013 ◽  
Vol 02 (04) ◽  
pp. 104-111 ◽  
Author(s):  
Donald Y. C. Lie ◽  
Vighnesh Das ◽  
Weibo Hu ◽  
Yenting Liu ◽  
Tam Nguyen

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