• DocumentCode
    3562275
  • Title

    Principal component analysis of body surface potential mapping in atrial fibrillation patients suggests additional ECG lead locations

  • Author

    Zeemering, Stef ; Lankveld, Theo A. R. ; Bonizzi, Pietro ; Crijns, Harry ; Schotten, Ulrich

  • Author_Institution
    Dept. of Physiol., CARIM, Maastricht, Netherlands
  • fYear
    2014
  • Firstpage
    893
  • Lastpage
    896
  • Abstract
    Atrial fibrillation (AF) is typically detected and analyzed in a non-invasive way using the standard 12-lead ECG. However, AF substrate complexity quantification may be suboptimal using conventional ECG locations. We analyzed high-density body surface potential maps of 75 patients in persistent AF to locate regions where AF complexity was predominantly expressed and to search for potential additional lead locations. Principal component analysis was applied to 1 minute of AF for each patient on the original ECG, TQ segments and extracted atrial activity (AA). Spatial complexity k0.95 was higher in AA or TQ segments than in ECG (median k0.95, AA: 13 components, TQ: 7, ECG: 2, p <; 0.001). Normalized variance described by the top 3 principal components was lower in AA and TQ segments (median %, AA: 85%, TQ: 87%, ECG: 99%, p <; 0.001). Maps of normalized component coefficient energy showed expression of major ECG components concentrated in the region covered by V1-V6, while the major TQ and AA components were more dispersed around the precordial leads, suggesting that non-invasive assessment of AF complexity by the standard 12-lead ECG is suboptimal. Placing additional leads around the precordial leads may improve non-invasive characterization of the AF substrate.
  • Keywords
    bioelectric potentials; electrocardiography; medical signal processing; principal component analysis; atrial fibrillation patients; atrial fibrillation substrate complexity quantification; body surface potential mapping; principal component analysis; standard 12-lead ECG; Complexity theory; Electrocardiography; Electrodes; Lead; Matrix decomposition; Principal component analysis; Standards;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in Cardiology Conference (CinC), 2014
  • ISSN
    2325-8861
  • Print_ISBN
    978-1-4799-4346-3
  • Type

    conf

  • Filename
    7043187