JOURNAL ARTICLE

A 60 $\mu$W 60 nV/$\surd$Hz Readout Front-End for Portable Biopotential Acquisition Systems

Refet Fırat YazıcıoğluPatrick MerkenRobert PuersChris Van Hoof

Year: 2007 Journal:   IEEE Journal of Solid-State Circuits Vol: 42 (5)Pages: 1100-1110   Publisher: Institute of Electrical and Electronics Engineers

Abstract

There is a growing demand for low-power, small-size and ambulatory biopotential acquisition systems. A crucial and important block of this acquisition system is the analog readout front-end. We have implemented a low-power and low-noise readout front-end with configurable characteristics for Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG) signals. Key to its performance is the new AC-coupled chopped instrumentation amplifier (ACCIA), which uses a low power current feedback instrumentation amplifier (U). Thus, while chopping filters the 1/f noise of CMOS transistors and increases the CMRR, AC coupling is capable of rejecting differential electrode offset (DEO) up to +/- 50 mV from conventional Ag/AgCl electrodes. The ACCIA achieves 120 dB CMRR and 57 nV/root Hz input-referred voltage noise density, while consuming 11.1 mu A from a 3 V supply. The chopping spike filter (CSF) stage filters the chopping spikes generated by the input chopper of ACCIA and the digitally controllable variable gain stage is used to set the gain and the bandwidth of the front-end. The front-end is implemented in a 0.5 mu m CMOS process. Total current consumption is 20 mu A from 3V.

Keywords:
Analog front-end Instrumentation amplifier Amplifier CMOS Front and back ends Electrical engineering Chopper Common-mode rejection ratio Noise (video) Programmable-gain amplifier Physics Operational amplifier Voltage Computer science Engineering

Metrics

375
Cited By
23.44
FWCI (Field Weighted Citation Impact)
39
Refs
1.00
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
ECG Monitoring and Analysis
Health Sciences →  Medicine →  Cardiology and Cardiovascular Medicine
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