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An ultra low energy biomedical signal processing system operating at near-threshold

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Author: Hulzink, J. · Konijnenburg, M. · Ashouei, M. · Breeschoten, A. · Berset, T. · Huisken, J. · Stuyt, J. · Groot, H. de · Barat, F. · David, J. · Ginderdeuren, J. van
Type:article
Date:2011
Source:IEEE Transactions on Biomedical Circuits and Systems, 6, 5, 546-554
Identifier: 461369
Article number: No.: 6104198
Keywords: Electronics · Biomedical signal processing · ElectroCardioGram (ECG) processing · Low power · Low voltage · Multi-power domain · Multi-voltage domain · Near-threshold design · Biomedical signal processing · Low Power · Low voltages · Multi-power domain · Multi-voltage · Bioelectric phenomena · Digital signal processing · Electric network synthesis · Electrocardiography · Microprocessor chips · Sensor nodes · Systems analysis · Wavelet transforms · Signal detection · High Tech Systems & Materials · Industrial Innovation · Mechatronics, Mechanics & Materials · HOL - Holst · TS - Technical Sciences

Abstract

This paper presents a voltage-scalable digital signal processing system designed for the use in a wireless sensor node (WSN) for ambulatory monitoring of biomedical signals. To fulfill the requirements of ambulatory monitoring, power consumption, which directly translates to the WSN battery lifetime and size, must be kept as low as possible. The proposed processing platform is an event-driven system with resources to run applications with different degrees of complexity in an energy-aware way. The architecture uses effective system partitioning to enable duty cycling, single instruction multiple data (SIMD) instructions, power gating, voltage scaling, multiple clock domains, multiple voltage domains, and extensive clock gating. It provides an alternative processing platform where the power and performance can be scaled to adapt to the application need. A case study on a continuous wavelet transform (CWT)-based heart-beat detection shows that the platform not only preserves the sensitivity and positive predictivity of the algorithm but also achieves the lowest energy/sample for ElectroCardioGram (ECG) heart-beat detection publicly reported today. © 2011 IEEE.