Ultra Low-Power Biomedical Signal Processing: An Analog Wavelet Filter Approach for Pacemakers

by Sandro Augusto Pavlik Haddad, Wouter A Serdijn

Overview

Often WT systems employ the discrete wavelet transform, implemented on a digital signal processor. However, in ultra low-power applications such as biomedical implantable devices, it is not suitable to implement the WT by means of digital circuitry due to the relatively high power consumption associated with the required A/D converter. Low-power analog realization of the wavelet transform enables its application in vivo, e.g. in pacemakers, where the wavelet transform provides a means to extremely reliable cardiac signal detection.

In Ultra Low-Power Biomedical Signal Processing we present a novel method for implementing signal processing based on WT in an analog way. The methodology presented focuses on the development of ultra low-power analog integrated circuits that implement the required signal processing, taking into account the limitations imposed by an implantable device.

Product Details

ISBN-13:	9789048180615
Publisher:	Springer Netherlands
Publication date:	12/08/2010
Series:	Analog Circuits and Signal Processing
Edition description:	Softcover reprint of hardcover 1st ed. 2009
Pages:	215
Product dimensions:	6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

1 Introduction. 1.1 Biomedical signal processing. 1.2 Biomedical applications of the wavelet transform. 1.3 Analog versus digital circuitry - a power consumption challenge for biomedical front-ends. 1.4 Objective and scope of this thesis. 1.5 Outline. 2 The Evolution of Pacemakers: An Electronics Perspective. 2.1 The Heart. 2.2 Cardiac Signals. 2.3 The history and development of cardiac pacing. 2.4 New Features in Modern Pacemakers. 2.5 Summary and Conclusions. 3 Wavelet versus Fourier analysis. 3.1 Introduction. 3.2 Fourier transform. 3.3 Windowing function. 3.4 Wavelet transform. 3.5 Signal Processing with Wavelet Transform. 3.6 Low-power analog wavelet filter design. 3.7 Conclusions. 4 Analog Wavelet filters: the need for approximation. 4.1 Introduction. 4.2 Complex First Order filters. 4.3 Pa&dacute;e Approximation in the Laplace domain. 4.4 L2 Approximation. 4.5 Other approaches for Wavelet bases approximation. 4.6 Discussion. 4.7 Conclusions. 5 Optimal State Space Descriptions. 5.1 State space description. 5.2 Dynamic Range. 5.3 Sparsity. 5.4 Sensitivity. 5.5 Conclusion. 6 Ultra Low-power Integrator Designs. 6.1 Gm-C filters. 6.2 Translinear (Log-domain) filters. 6.3 Class-A log-domain filter design examples. 6.4 Low-power Class-AB Sinh Integrators. 6.5 Discussion. 6.6 Conclusions. 7 Ultra Low-power Biomedical System Designs. 7.1 Dynamic Translinear Cardiac Sense Amplifier for Pacemakers. 7.2 QRS-complex wavelet detection using CFOS. 7.3 Wavelet filter designs. 7.4 Morlet Wavelet Filter. 7.5 Conclusions. 8 Conclusions and Future Research. 8.1 Future Research. A High-Performance Analog Delays. A.1 Bessel-Thomson approximation. A.2 Pa&dacute;e approximation. A.3 Comparison of Bessel-Thomson and Pa&dacute;e approximation delay filters. A.4 Gaussian Time-domain impulse-response method. B Model reduction - the BalancedTruncation method. C Switched-Capacitor Wavelet Filters. D Ultra-Wideband Circuit Designs. D.1 Impulse Generator for Pulse Position Modulator. D.2 A Delay Filter for an UWB Front-End. D.3 A FCC Compliant Pulse Generator for UWB Communications. Summary.