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A 2.2 nW Analog Electrocardiogram Processor Based on Stochastic Resonance Achieving a 99.94% QRS Complex Detection Sensitivity | IEEE Journals & Magazine | IEEE Xplore

A 2.2 nW Analog Electrocardiogram Processor Based on Stochastic Resonance Achieving a 99.94% QRS Complex Detection Sensitivity


Abstract:

This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band...Show More

Abstract:

This paper presents an ultra-low power electrocardiogram (ECG) processor that can detect QRS-waves in real time as the data streams in. The processor performs out-of-band noise suppression via a linear filter, and in-band noise suppression via a nonlinear filter. The nonlinear filter also enhances the QRS-waves by facilitating stochastic resonance. The processor identifies the QRS-waves on noise-suppressed and enhanced recordings using a constant threshold detector. For energy-efficiency and compactness, the processor exploits current-mode analog signal processing techniques, which significantly reduces the design complexity when implementing the second-order dynamics of the nonlinear filter. The processor is designed and implemented in TSMC 65 nm CMOS technology. In terms of detection performance, the processor achieves an average {\bm{F}}1 = 99.88{\bm{\% }} over the MIT-BIH Arrhythmia database and outperforms all previous ultra-low power ECG processors. The processor is the first that is validated against noisy ECG recordings of MIT-BIH NST and TELE databases, where it achieves better detection performances than most digital algorithms run on digital platforms. The design has a footprint of 0.08 mm2 and dissipates 2.2 nW when supplied by a single 1V supply, making it the first ultra-low power and real-time processor that facilitates stochastic resonance.
Published in: IEEE Transactions on Biomedical Circuits and Systems ( Volume: 17, Issue: 1, February 2023)
Page(s): 33 - 44
Date of Publication: 10 January 2023

ISSN Information:

PubMed ID: 37018643

Funding Agency:


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