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Hardware aware algorithm performance and the low power continuous wavelet transform

Published: 26 October 2011 Publication History

Abstract

Highly miniaturised, wearable, physiological sensors require algorithms for the automated analysis of the collected signal. To reduce the total sensor power consumption in many situations the automated analysis is best carried on the sensor device itself and this online signal processing needs to be both accurate (in terms of correct detections and false detections) and also be implemented using very low power consumption circuits. However, reducing the circuit power consumption potentially impacts the algorithm performance. Hardware aware algorithms need to take this into account.
This paper takes a previously reported 60 pW Continuous Wavelet Transform (CWT) circuit and investigates the impact of this circuit on a CWT-based algorithm for providing real-time EEG data reduction. An analytical model describing the measured variations in CWT response between different microchips is built, and this used in Matlab simulations of the EEG algorithm. Compared to using an ideal CWT stage, the impact of the modelled CWT circuit is negligible, resulting in only a 0.001 reduction in ROC-like performance area.

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  • (2019)Optimization and Implementation of Wavelet-based Algorithms for Detecting High-voltage Spindles in Neuron SignalsACM Transactions on Embedded Computing Systems10.1145/332986418:5(1-16)Online publication date: 18-Jul-2019

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    cover image ACM Other conferences
    ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
    October 2011
    949 pages
    ISBN:9781450309134
    DOI:10.1145/2093698
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Technical University of Catalonia Spain: Technical University of Catalonia (UPC), Spain
    • River Publishers: River Publishers
    • CTTC: Technological Center for Telecommunications of Catalonia
    • CTIF: Kyranova Ltd, Center for TeleInFrastruktur

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    Published: 26 October 2011

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    • (2019)Optimization and Implementation of Wavelet-based Algorithms for Detecting High-voltage Spindles in Neuron SignalsACM Transactions on Embedded Computing Systems10.1145/332986418:5(1-16)Online publication date: 18-Jul-2019

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