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

Annu Int Conf IEEE Eng Med Biol Soc. 2010:2010:634-7. doi: 10.1109/IEMBS.2010.5627261.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

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