Advertisement

Using Standardized Numerical Scores for the Display and Interpretation of Biomedical Data

Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 696)

Abstract

The ability to review and analyze large amounts of data reliably and cost-effectively is important in both biomedical research and clinical care. We hypothesized that converting raw digital data to standard scores (Z scores) and gray-scale them based on their corresponding P values can be used to accomplish this. In Part 1 of the study, we recorded continuous digital electrocardiographic (ECG) and heart sound data from a subject undergoing acute anterior myocardial infarction (MI). We then computed Z scores of the digital data using the means and standard deviations of the data obtained during the pre-infarction period. In Part 2 of the study, we analyzed the digital ECG data from 576 subjects who had undergone coronary angiography and left ventriculography for the evaluation of possible coronary disease. We used the durations of Q waves in Lead aVF and of the initial R waves in Lead V2 as the ECG criteria of prior inferior and anterior MI, respectively. We calculated Z scores for these durations using the means and standard deviations of the subjects who had no angiographic evidence of coronary disease. Results show that in Part 1 of the study, the continuous recording of the gray-scale Z scores produced a highly intuitive display of the direction and statistical significance of simultaneous changes in five quantitative parameters known to be important for the detection and assessment of acute MI. In Part 2 of the study, the use of gray-scale Z scores revealed, in each of three subgroups, the distributions of subjects who met ECG criteria for inferior and anterior MI, respectively. Analyzing the Z scores to calculate diagnostic performances yielded results similar to those obtained using receiver-operating characteristic curves of the raw data. We conclude that the use of gray-scale Z scores is a highly efficient and statistically meaningful way to display diagnostic data produced by both continuous and individual recordings.

Keywords

Myocardial Infarction Diagnostic Data Rapid Review Biomedical Data Wave Duration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Zuber M, Kipfer P, Attenhofer Jost CH. Usefulness of acoustic cardiography to resolve ambiguous values of B-type natriuretic peptide levels in patients with suspected heart failure. Am J Cardiol 2007;100(5):866–869Google Scholar
  2. 2.
    Marcus GM, Gerber IL, McKeown BH, Vessey JC, Jordan MV, Huddleston M, McCulloch CE, Foster E, Chatterjee K, Michaels AD. Association between phonocardiographic third and fourth heart sounds and objective measures of left ventricular function. JAMA 2005;293(18):2238–2244PubMedCrossRefGoogle Scholar
  3. 3.
    Gerber IL, McKeown BH, Marcus G, Vessy J, Jordan MV, Huddleston M, et al. The third and fourth heart sounds are highly specific markers for elevated left ventricular filling pressure and reduced ejection fraction. J Card Fail 2004;10(4):S36CrossRefGoogle Scholar
  4. 4.
    Roos M, Toggweiler S, Zuber M, Jamshidi P, Erne P. Acoustic cardiographic parameters and their relationship to invasive hemodynamic measurements in patients with left ventricular systolic dysfunction. Congest Heart Fail 2006;12(4 Suppl. 1):19–24Google Scholar
  5. 5.
    Warner R, Hill NE, Sheehe PR, Mookherjee S, Fruehan CT, Smulyan H. Improved electrocardiographic criteria for the diagnosis of inferior myocardial infarction. Circulation 1982;66:422–428PubMedGoogle Scholar
  6. 6.
    Warner R, Hill N, Mookherjee S, Smulyan H. Electrocardiographic criteria for the diagnosis of combined inferior myocardial infarction and left anterior hemiblock. Am J Cardiol 1983;51:718–722PubMedCrossRefGoogle Scholar
  7. 7.
    Warner R, Reger M, Hill N, Mookherjee S, Smulyan, H. Electrocardiographic criteria for the diagnosis of anterior myocardial infarction. Importance of the duration of precordial R waves. Am J Cardiol 1983;52:690–692Google Scholar
  8. 8.
    Warner RA, Olicker AL, Haisty WK, Hill NE, Selvester RH, Wagner GS. The importance of accounting for the variability of electrocardiographic data among diagnostically similar patients. Am J Cardiol 2000;86:1238–1240PubMedCrossRefGoogle Scholar
  9. 9.
    Thatcher RW. Normative EEG databases and EEG biofeedback. J Neurother 1998;2(4):8–39CrossRefGoogle Scholar
  10. 10.
    Berger JO, Strawderman WE. Choice of hierarchical priors: admissibility in estimation of normal means. Ann Stat 1996;24(3):931–951CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Laboratory for Logic and Experimental PhilosophySimon Fraser UniversityBurnabyCanada

Personalised recommendations