title: An Analog Circuit Approximation of the Discrete Wavelet Transform for Ultra Low Power Signal Processing in Wearable Sensor Nodes.
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 g(m)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.
authors.affiliation: School of Electrical and Electronic Engineering, The University of Manchester, Manchester M13 9PL, UK. alex.casson@manchester.ac.uk.
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Casson
mesh_terms: D003198:Computer Simulation; D004562:Electrocardiography / Q000295:instrumentation*; D004569:Electroencephalography / Q000295:instrumentation*; D006801:Humans; D008962:Models, Theoretical; D058067:Wavelet Analysis
keywords: analog signal processing; discrete wavelet transform; gmC filters; low power signal processing