The effect of lossy ECG compression on QRS and HRV feature extraction

Niall Twomey, Noel Walsh, Orla Doyle, Brian McGinley, Martin Glavin, Edward Jones, W P Marnane

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

7 Citations (Scopus)

Abstract

This paper describes the performance of beat detection and heart rate variability (HRV) feature extraction on electrocardiogram signals which have been compressed and reconstructed with a lossy compression algorithm. The set partitioning in hierarchical trees (SPIHT) compression algorithm was used with sixteen compression ratios (CR) between 2 and 50 over the records of the MIT/BIH arrhythmia database. Sensitivities and specificities between 99% and 85% were computed for each CR utilised. The extracted HRV features were between 99% and 82% similar to the features extracted from the annotated records. A notable accuracy drop over all features extracted was noted beyond a CR of 30, with falls of 10% accuracy beyond this compression ratio.
Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages634-7
Number of pages4
Volume2010
ISBN (Print)978-1-4244-4123-5
DOIs
Publication statusPublished - 2010

Publication series

NameIEEE Engineering in Medicine and Biology Society
ISSN (Print)1557-170X

Keywords

  • Sensitivity and Specificity
  • Artifacts
  • Signal Processing, Computer-Assisted
  • Reproducibility of Results
  • Humans
  • Diagnosis, Computer-Assisted
  • Algorithms
  • Artificial Intelligence
  • Electrocardiography
  • Sample Size
  • Pattern Recognition, Automated
  • Data Compression
  • Arrhythmias, Cardiac

Cite this