Annals of Biomedical Engineering

, Volume 23, Issue 5, pp 553–561 | Cite as

Multiresolution wavelet analysis of the body surface ECG before and after angioplasty

  • Boris Gramatikov
  • Sun Yi-chun
  • Herve Rix
  • Pere Caminal
  • Nitish V. Thakor
Invited Articles


Electrocardiographic recordings of patients with coronary artery stenosis, made before and after angioplasty, were analyzed by the multiresolution wavelet transform (MRWT) technique. The MRWT decomposes the signal of interest into its coarse and detail components at successively finer scales. MRWT was carried out on different leads in order to compare the P-QRS-T complex from recordings made before with those made after percutaneous transluminal coronary angioplasty (PTCA). ECG signals before and after successful PTCA procedures show distinctive changes at certain scales, thus helping to identify whether the procedure has been successful. In six patients who underwent right coronary artery PTCA, varying levels of reperfusion were achieved, and the changes in the detail components of ECG were shown to correlate with the successful reperfusion. The detail components at scales 5 and 6, corresponding approximately to the frequencies in the range of 2.3–8.3 Hz, are shown to be the most sensitive to ischemia-reperfusion changes (p<0.05). The same conclusion was reached by synthesizing the post-PTCA signals from pre-PTCA signals with the help of these detail components. For on-line monitoring a vector plot, analogous to vector cardiogram, of the two most sensitive MRWT detail components is proposed. Thus, multiresolution analysis of ECG may be useful as a monitoring and diagnostic tool during angioplasty procedures.


ECG wavelets multiresolution ischemia occlusion reperfusion angioplasty PTCA 


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Copyright information

© Biomedical Engineering Society 1995

Authors and Affiliations

  • Boris Gramatikov
    • 1
  • Sun Yi-chun
    • 4
  • Herve Rix
    • 2
  • Pere Caminal
    • 3
  • Nitish V. Thakor
    • 4
  1. 1.Department of Biomedical EngineeringCLEMA, Medical FacultySophiaBulgaria
  2. 2.Laboratoire Signaux et SystemsUniversite de NiceNiceFrance
  3. 3.Institut de CiberneticaUniversitat Pollitecnica de Catalunya-CSICBarcelonaSpain
  4. 4.Biomedical EngineeringJohns Hopkins Medical SchoolBaltimoreUSA

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