JOURNAL ARTICLE

Analog Front-End Input-Impedance Boosting Techniques for Bio-Potential Monitoring—A Review

Feng YanJingjing Liu

Year: 2024 Journal:   IEEE Transactions on Instrumentation and Measurement Vol: 73 Pages: 1-14   Publisher: Institute of Electrical and Electronics Engineers

Abstract

Physiological information provided by bio-potential signals is essential and pivotal for biomedical sensors. As the first stage in the signal chain of the sensor, impedance matching of the analog front-end (AFE) is critical for determining signal integrity. AFEs require high input impedance to minimize the effects of electrode impedance mismatch. A significant problem is that the input impedance is affected by parasitic capacitances from the integrated circuit (IC) package, electrode cables, and internal chips. This paper aims to summarize the development of AFE impedance boosting techniques for bio-potential monitoring. The scope of this work includes a review of common architectures for instrumentation amplifiers (IA), mechanisms for input impedance reduction, and a comparison of impedance boosting factors (IBF) employed to evaluate the efficiency of impedance enhancement. Techniques schemes for boosting input impedance in architectures are then surveyed and the contributions and drawbacks of these techniques are discussed. Finally, the state-of-the-art designs of AFE are compared and summarized, along with an analysis of the trend toward enhanced input impedance in bio-electrical signal acquisition. These provide useful references for AFE designers.

Keywords:
Boosting (machine learning) Electrical impedance Front and back ends Analog front-end Electronic engineering Electrical engineering Focused Impedance Measurement Computer science Engineering Mechanical engineering Artificial intelligence CMOS

Metrics

13
Cited By
4.78
FWCI (Field Weighted Citation Impact)
132
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Analog and Mixed-Signal Circuit Design
Physical Sciences →  Engineering →  Biomedical Engineering
Neuroscience and Neural Engineering
Life Sciences →  Neuroscience →  Cellular and Molecular Neuroscience
Analytical Chemistry and Sensors
Physical Sciences →  Chemical Engineering →  Bioengineering
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