Usefulness of adaptive correlation filter for detecting QRS waves from noisy electrocardiograms

Msaya Kisohara, Yuto Masuda, Emi Yuda, Junichiro Hayano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electrocardiogram (ECG) is the most successful physiological signal that is measured continuously in freely moving humans for a long time and R-R intervals obtained from ECG is the standard measure for analyzing heart rate variability. However, ECG signals under daily activities often contain various noises, including those caused by theoretically inevitable sources, such as electromyograms and cardiac axial fluctuations with respiration and postural changes. As the result, even automated ECG analyzers used for clinical purposes still require the careful editing and corrections of QRS detections errors by skilled operators, which causes both economical and time consuming burden. Given the recent wide-spread of wearable ECG monitoring and its potentially life-long longitudinal data collections, the development of highly reliable QRS wave detection algorithms has become increasingly important. Therefore, this study focused on improving QRS detection accuracy in electrocardiogram with noise mixed, focusing on the usefulness of adaptive correlation filter.

Original languageEnglish
Title of host publication2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages105-107
Number of pages3
ISBN (Electronic)9781728105437
DOIs
Publication statusPublished - 2019 Mar
Externally publishedYes
Event1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019 - Osaka, Japan
Duration: 2019 Mar 122019 Mar 14

Publication series

Name2019 IEEE 1st Global Conference on Life Sciences and Technologies, LifeTech 2019

Conference

Conference1st IEEE Global Conference on Life Sciences and Technologies, LifeTech 2019
CountryJapan
CityOsaka
Period19/3/1219/3/14

Keywords

  • Adaptive correlation filter
  • Biomedical
  • Electrocardiogram
  • Heart rate variability
  • Signal processing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Health Informatics
  • Neuroscience (miscellaneous)
  • Computer Science Applications
  • Biomedical Engineering

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