Cardiovascular Engineering

, Volume 9, Issue 1, pp 18–26

Spectral Energy of ECG Morphologic Differences to Predict Death

  • Zeeshan Syed
  • Phil Sung
  • Benjamin M. Scirica
  • David A. Morrow
  • Collin M. Stultz
  • John V. Guttag
Original Paper

DOI: 10.1007/s10558-009-9066-3

Cite this article as:
Syed, Z., Sung, P., Scirica, B.M. et al. Cardiovasc Eng (2009) 9: 18. doi:10.1007/s10558-009-9066-3

Abstract

Unstable conduction system bifurcations following ischemia and infarction are associated with variations in the electrocardiographic activity spanning the heart beat. In this paper, we investigate a spectral energy measure of morphologic differences (SE-MD) that quantifies aspects of these changes. Our measure uses a dynamic time-warping approach to compute the time-aligned morphology differences between pairs of successive sinus beats in an electrocardiographic signal. While comparing beats, the entire heart beat signal is analyzed in order to capture changes affecting both depolarization and repolarization. We show that variations in electrocardiographic activity associated with death can be distinguished by their spectral characteristics. We developed the SE-MD metric on holter data from 764 patients from the TIMI DISPERSE2 dataset and tested it on 600 patients from the TIMI MERLIN dataset. In the test population, high SE-MD was strongly associated with death over a 90 day period following non-ST-elevation acute coronary syndrome (HR 10.45, p < 0.001) and showed significant discriminative ability (c-statistic 0.85). In comparison with heart rate variability and deceleration capacity, SE-MD was also the most significant predictor of death in the study population. Furthermore, SE-MD had low correlation with these other measures, suggesting that complementary use of the risk variables may allow for more complete assessment of cardiac health.

Keywords

Electrocardiogram (ECG) Risk stratification Acute coronary syndromes Heart rate variability Deceleration capacity Morphologic differences 

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Zeeshan Syed
    • 1
    • 2
  • Phil Sung
    • 1
  • Benjamin M. Scirica
    • 3
  • David A. Morrow
    • 3
  • Collin M. Stultz
    • 1
    • 2
  • John V. Guttag
    • 1
  1. 1.Department of Electrical Engineering and Computer Science, MITCambridgeUSA
  2. 2.Harvard-MIT Division of Health Sciences and TechnologyCambridgeUSA
  3. 3.TIMI Study Group, Cardiovascular DivisionBrigham and Women’s HospitalBostonUSA

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