DocumentCode
1234584
Title
An Ultra Low-Power CMOS Automatic Action Potential Detector
Author
Gosselin, Benoit ; Sawan, Mohamad
Author_Institution
Electr. Eng. Dept., Ecole Polytech. de Montreal, Montreal, QC, Canada
Volume
17
Issue
4
fYear
2009
Firstpage
346
Lastpage
353
Abstract
We present a low-power complementary metal-oxide semiconductor (CMOS) analog integrated biopotential detector intended for neural recording in wireless multichannel implants. The proposed detector can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element. This strategy improves detected waveforms integrity and prompts for better performance in neural prostheses. The delay element is implemented with a low-power continuous-time filter using a ninth-order equiripple allpass transfer function. All circuit building blocks use subthreshold OTAs employing dedicated circuit techniques for achieving ultra low-power and high dynamic range. The proposed circuit function in the submicrowatt range as the implemented CMOS 0.18-mum chip dissipates 780 nW, and it features a size of 0.07 mm2. So it is suitable for massive integration in a multichannel device with modest overhead. The fabricated detector succeeds to automatically detect APs from underlying background activity. Testbench validation results obtained with synthetic neural waveforms are presented.
Keywords
CMOS analogue integrated circuits; bioelectric potentials; medical signal detection; neurophysiology; prosthetics; analog integrateddetector; complementary metal-oxide semiconductor; continuous-time filter; neural prostheses; neural recording; power 780 nW; size 0.18 mum; ultra low-power CMOS automatic action potential detector; wireless multichannel implants; Biopotential detection; energy operator; neural recording; neuroprosthetics; ultra low-power circuits design; Action Potentials; Amplifiers, Electronic; Electric Power Supplies; Equipment Design; Equipment Failure Analysis; Miniaturization; Nerve Net; Pattern Recognition, Automated; Reproducibility of Results; Semiconductors; Sensitivity and Specificity; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher
ieee
ISSN
1534-4320
Type
jour
DOI
10.1109/TNSRE.2009.2018103
Filename
4813269
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