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A new energy-efficient pre-diagnosing ECG transmission technique for BASN

Published: 26 October 2011 Publication History

Abstract

Electrocardiograms (ECG) provide invaluable insight into the conditions of the heart and are widely used for diagnosing cardiac diseases. Recent advances in miniature sensors and low-power wireless transmitters make body area sensor networks (BASN) a compelling platform for mobile ECG monitoring. However, energy efficiency is still one of the major issues in BASN, which are typically battery-powered. In this paper, we present an innovative and energy-efficient Pre-Diagnosing ECG Transmission Technique for BASN. In our technique, we explore the differences of ECG data in terms of its importance for medical diagnosis. A self-learning ECG classification algorithm is designed to classify the sensed ECG data into the three classes of abnormal heart beats, unknown heart beats and normal heart beats. Subsequently, the communication resources are allocated differently on these heart beat classes so that communication energy can be saved without affecting the cardiac disease monitoring and diagnosis. According to our test results, about 80% to 100% classification accuracy can be achieved, with 0% misses in abnormal heart beats, while saving about 76% of energy compared with non-classifying transmission techniques in transmitting normal heart beats.

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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]

Sponsors

  • Universitat Pompeu Fabra
  • IEEE
  • 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2011

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Author Tags

  1. ECG classification
  2. body area sensor network
  3. energy-efficient
  4. pre-diagnosing

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  • Research-article

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ISABEL '11
Sponsor:
  • Technical University of Catalonia Spain
  • River Publishers
  • CTTC
  • CTIF

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