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
Link To Document :
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