Publication

Ultra-low-power ECG front-end design based on compressed sensing

Abstract

Ultra-low-power design has been a challenging area for design of the sensor front-ends especially in the area of Wireless Body Sensor Nodes (WBSN), where a limited amount of power budget and hardware resources are available. Since introduction of Compressed Sensing, there has been a challenge to design CS-based low-power readout devices for different applications and among all for biomedical signals. Till now, different proposed realizations of the digital CS prove the suitability of using CS as an efficient low-power compression technique for compressible biomedical signals. However, these works mainly take advantages of only one aspect of the benefits of the CS. In this type of works, CS is usually used as a very low cost and easy to implement compression technique. This means that we should acquire the signal with traditional limitations on the bandwidth (BW) and later compresses it. However, the main power of the CS, which lies on the efficient data acquisition, remains untouched. Building on our previous work [1], where the suitability of the CS is proven for the compression of the ECG signals, and our investigation on ultra-low-power CS-based A2I devices [2] , here in this paper we propose a fully redesigned complete CS-based “Analog-to-information” (A/I) front-end for ECG signals. Our results show that proposed hybrid design easily outperforms the traditional implementation of CS with more than 11 times fold reduction in power consumption compared to standard implementation of CS. Moreover our design shows a very promising performance specially in high compression ratio.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.