An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.

Alexander Casson

doi:10.3390/s151229897

Abstract

Ultra low power signal processing is an essential part of all sensor nodes, and particularly so in emerging wearable sensors for biomedical applications. Analog signal processing has an important role in these low power, low voltage, low frequency applications, and there is a key drive to decrease the power consumption of existing analog domain signal processing and to map more signal processing approaches into the analog domain. This paper presents an analog domain signal processing circuit which approximates the output of the Discrete Wavelet Transform (DWT) for use in ultra low power wearable sensors. Analog filters are used for the DWT filters and it is demonstrated how these generate analog domain DWT-like information that embeds information from Butterworth and Daubechies maximally flat mother wavelet responses. The Analog DWT is realised in hardware via C circuits, designed to operate from a 1.3 V coin cell battery, and provide DWT-like signal processing using under 115 nW of power when implemented in a 0.18 μm CMOS process. Practical examples demonstrate the effective use of the new Analog DWT on ECG (electrocardiogram) and EEG (electroencephalogram) signals recorded from humans.

Highlights

Ultra low power signal processing is an integral part of all modern sensor nodes, and so in emerging wearable electronics for medical applications which need to be easy-to-use, robust and reliably always work [1]
No equivalent to the decimation stage of the Discrete Wavelet Transform (DWT) is present in this work, the results show that the Analog Discrete Wavelet Transform (ADWT) output is a continuous time estimation of the DWT coefficients and not Non-Decimated Wavelet Transform (NDWT) coefficients, or DWT signals.) In this paper a Butterworth mother wavelet is used due to its potential to estimate the output from Daubechies mother wavelets in the analog domain (Section 2.3) and its suitability for very low power implementation (Section 4)
This paper has presented a new low power analog approach for implementing the Discrete Wavelet Transform using under 115 nW of power

Summary

Introduction

Ultra low power signal processing is an integral part of all modern sensor nodes, and so in emerging wearable electronics for medical applications which need to be easy-to-use, robust and reliably always work [1]. In 2010 authors in the IEEE Signal Processing magazine posed the question: “What does ultra low power consumption mean?”; and came to the conclusion that it is where the “power source lasts longer than the useful life of the product” [2]. This is exactly what is required for creating truly ubiquitous and wearable sensors. [3] summaries the benefits of analog processing as “analog computation is more energy- and area-efficient at the cost of its limited accuracy, whereas digital computation is more versatile and derives greater benefits from technology scaling”. This mirrors earlier findings from [2] that “the old thought that we would be able to virtually eliminate analog and do everything in digital is dying away and we have the advantage of making tradeoffs between digital and analog implementation for SP [Signal Processing].” Further examples on the role of analog processing can be found in [2,3,4,5,6,7,8,9,10,11]

Methods

Results

Conclusion