Cardiovascular Engineering

, Volume 9, Issue 1, pp 18–26 | Cite as

Spectral Energy of ECG Morphologic Differences to Predict Death

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


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.


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



We would like to thank Gari Clifford for providing tools from Physionet for preprocessing ECG signals and for his technical input on heart rate variability metrics, and Dorothy Curtis for helping with the computational needs for this project. This work was supported, in part, by the Center for Integration of Medicine and Innovative Technology (CIMIT), the Harvard-MIT Division of Health Sciences and Technology (HST), and the Industrial Technology Research Institute (ITRI).


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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
    Email author
  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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