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Computer Applications in the Interpretation of the Exercise Electrocardiogram

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Abstract

The exercise electrocardiogram remains the noninvasive diagnostic test of first choice in patients with coronary artery disease. While new technology offers novel diagnostic possibilities and the ability to assess patients unsuitable for exercise testing, no other investigation has to this point furnished the quality of functional information and value-for-predictive accuracy of exercise electrocardiography.

In this article, we describe how this central position in the work up of the cardiac patient has been secured through the evolution of the microprocessor. Particularly important has been its ability to harness and present large volumes of raw data, to derive and manipulate multivariate equations for diagnostic prediction, and to run ‘expert’ systems which can pool demographic and exercise test data, calculate risk scores, and prompt the nonexpert with advice on current management. These key features explain the pivotal role of the exercise test in the diagnostic, and increasingly prognostic, armoury of the cardiovascular clinician.

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References

  1. Waller AD. Introductory address on the electromotive properties of the human heart. BMJ 1888; 2: 751–4

    Article  PubMed  CAS  Google Scholar 

  2. Rautaharju PM. A hundred years of progress in electrocardiography. 1: Early contributions from Waller to Wilson. Can J Cardiol 1987; 3: 362–74

    PubMed  CAS  Google Scholar 

  3. Einthoven W. Weiteres uber das elektrokardiogram. Arch Ges Physiol Menschen Thiere 1908; 122: 517

    Article  Google Scholar 

  4. Hyman A. Charles Babbage: pioneer of the computer. Princeton (NJ): Princeton University Press, 1982

    Google Scholar 

  5. Babbage H. Babbage’s calculating engines: being a collection of papers relating to them, their history, and construction. London: E & FN Spoon, 1889

    Google Scholar 

  6. Kelvin WT-L. Scientific papers: physics, chemistry, astronomy, geology, with introductions, notes and illustrations. Evening lecture to the British Association; 1882 Aug 25; Southampton. New York: PF Collier & Son, 1910

    Google Scholar 

  7. Bousfield G. Angina Pectoris. Lancet 1918; II: 457

    Article  Google Scholar 

  8. Feil H, Siegel M. Electrocardiographic changes during attacks of angina pectoris. Am J Med Sci 1928; 175: 235

    Article  Google Scholar 

  9. Goldhammer S, Scherf D. Elektrokardiographische untersuchungen bei kranken mit angina perctoris (’ambulatorischer Typus’). Ztschr f Klin Med 1932; 122: 134

    Google Scholar 

  10. Froelicher V, Fearon W, Ferguson C, et al. Lessons learned from the standard exercise ECG Test. Chest 1999; 116 (5): 1442–51

    Article  PubMed  CAS  Google Scholar 

  11. Rochmis P, Blackburn H. Exercise tests: a survey of procedures, safety, and litigation experience in approximately 170 000 tests. JAMA 1971; 217: 1061–6

    Article  PubMed  CAS  Google Scholar 

  12. Gibbons L, Blair S, Kohl H, et al. The safety of maximal exercise testing. Circulation 1989; 80: 846–52

    Article  PubMed  CAS  Google Scholar 

  13. Michaelides A, Psomadaki Z, Dilaveris P, et al. Improving detection of coronary artery disease by exercise electrocardiography with the use of right precordial leads. N Engl Med 1999; 340: 340–5

    Article  CAS  Google Scholar 

  14. Pipberger H. Twenty years ECG data processing: what has been accomplished. In: Antaloczy Z, editor. Modern electrocardiology. Amsterdam: Excerpta Medica, 1978: 159–63

    Google Scholar 

  15. Willems J. Computer analysis of the electrocardiogram. In: Macfarlane P, Lawrie TV, editors. Comprehensive electrocardiology: theory and practice in health and disease. New York: Pergamon Press, 1989: 1139–76

    Google Scholar 

  16. Taback L, Marden E, Mason H, et al. Digital recording of electrocardiographic data for analysis by digital computer. Med Electronics 1959; 6: 167–71

    Google Scholar 

  17. Pipberger H, Arms R, Stallmann F. Automatic screening of normal and abnormal electrocardiograms by means of a digital electronic computer. Proc Soc Exp Biol Med 1961; 106: 130–2

    PubMed  CAS  Google Scholar 

  18. Stallmann F, Pipberger H. Automatic recognition of electrocardiographic waves by digital computer. Circ Res 1961; 9: 1138–43

    Article  PubMed  CAS  Google Scholar 

  19. Caceres C, Steinberg C, Abraham S, et al. Computer extraction of electrocardiographic parameters. Circulation 1962; 25: 356–62

    Article  PubMed  CAS  Google Scholar 

  20. Froelicher V. Special Methods: computerized ECG analysis. In: Froelicher V, Myers J, editors. Exercise and the heart. 4th ed. Philadelphia: Saunders/Mosby, 1999

    Google Scholar 

  21. Moore G. An Update on Moore’s Law, 1997 [online]. Available from: URL: http://developer.intel.com/pressroom/archive/speeches/gem93097.html [Accessed 2000 Jun 29]

    Google Scholar 

  22. Moore G. Interview with Gordon Moore [online]. Available from: URL: http://www.sciam.com/interview/moore/092297moorel.html [Accessed 2000 Aug 22]

    Google Scholar 

  23. Willems J, Lesaffre E, Pardaens J. Comparison of the classification ability of the electrocardiogram and vectorcardiogram. Am J Cardiol 1987; 59: 119–24

    Article  PubMed  CAS  Google Scholar 

  24. Jain U, Rautaharju PM. Diagnostic accuracy of the conventional 12-lead and the orthogonal Frank-lead electrocardiograms in detection of myocardial infarctions with classifiers using continuous and Bernoulli features. J Electrocardiol 1980; 13: 159–66

    Article  PubMed  CAS  Google Scholar 

  25. Macfarlane PW, Melville DI, Horton MR, et al. Comparative evaluation of the IBM(12-lead) and Royal Infirmary (orthogonal three-lead) ECG computer programs. Circulation 1981; 63: 354–9

    Article  PubMed  CAS  Google Scholar 

  26. Whincup PH, Wannamethee G, Macfarlane PW, et al. Resting electrocardiogram and risk of coronary heart disease in middle-aged British men. J Cardiovasc Risk 1995; 2: 533–43

    Article  PubMed  CAS  Google Scholar 

  27. Kornreich F, Rautaharju P. The missing waveform and diagnostic information in the standard 12 lead electrocardiogram. J Electrocardiol 1981; 14: 341–50

    Article  PubMed  CAS  Google Scholar 

  28. Blomqvist G. The Frank lead exercise electrocardiogram. Acta Med Scand 1965; 178: 1–98

    PubMed  CAS  Google Scholar 

  29. Rautaharju P, Punsar S, Blackburn H, et al. Waveform patterns in Frank-lead rest and exercise electrocardiograms of healthy elderly men. Circulation 1973; 48: 541–8

    Article  PubMed  CAS  Google Scholar 

  30. Simonson E. Electrocardiographic stress tolerance tests. Progress Cardiovasc Dis 1970; 13: 269–92

    Article  CAS  Google Scholar 

  31. Simoons M, Hugenholtz P. Gradual changes of ECG waveform during and after exercise in normal subjects. Circulation 1975; 52: 570–7

    Article  PubMed  CAS  Google Scholar 

  32. Wolthuis R, Froelicher V, Hopkirk A, et al. Normal electrocardiographic waveform characteristics during treadmill exercise testing. Circulation 1979; 60: 1028–35

    Article  PubMed  CAS  Google Scholar 

  33. Bonoris P, Greenberg P, Castellanet M, et al. Significance of changes in R wave amplitude during treadmill stress testing: angiographic correlation. Am J Cardiol 1978; 41: 846–51

    Article  PubMed  CAS  Google Scholar 

  34. Mark D, Hlatky M, Lee K, et al. Localizing coronary artery obstructions with the exercise treadmill test. Ann Intern Med 1987; 106: 53–5

    PubMed  CAS  Google Scholar 

  35. Cleland J, Findlay I, Gilligan D, et al. The essentials of exercise electrocardiography. London: Current Medical Literature, 1993

    Google Scholar 

  36. Li D, Li CY, Yong AC, et al. Source electrocardiographic ST changes in subendocardial ischemia. Circ Res 1998; 82: 957–70

    Article  PubMed  CAS  Google Scholar 

  37. Simoons ML. Optimal measurements for detection of coronary artery disease by exercise electrocardiography. Comput Biomed Res 1977; 10: 483–99

    Article  PubMed  CAS  Google Scholar 

  38. McHenry P, Stowe D, Lancaster M. Computer quantitation of the ST segment response during maximal treadmill exercise. Circulation 1968; 38: 691–702

    Article  PubMed  CAS  Google Scholar 

  39. Sketch MH, Mohiuddin SM, Nair CK, et al. Automated and nomographic analysis of exercise tests. JAMA 1980; 243: 1052–5

    Article  PubMed  CAS  Google Scholar 

  40. Sheffield L, Holt T, Lester F, et al. On-line analysis of the exercise ECG. Circulation 1969; 40: 935–44

    Article  Google Scholar 

  41. Forlini F, Cohn K, Langston M. ST segment isolation and quantification as a means of improving diagnostic accuracy in treadmill stress testing. Am Heart J 1975; 90: 431–8

    Article  PubMed  Google Scholar 

  42. Ascoop CA, Distelbrink CA, De Lang PA. Clinical value of quantitative analysis of ST slope during exercise. Br Heart J 1977; 39: 212–7

    Article  PubMed  CAS  Google Scholar 

  43. Kligfield P, Ameisen O, Okin P. Heart rate adjustment of ST segment depression for improved detection of coronary artery disease. Circulation 1989; 79: 245–55

    Article  PubMed  CAS  Google Scholar 

  44. Elamin M, Mary D, Smith D, et al. Prediction of severity of coronary artery disease using slope of submaximal ST segment/heart rate relationship. Cardiovasc Res 1980; 14: 681–91

    Article  PubMed  CAS  Google Scholar 

  45. Hollenberg M, Budge W, Wisneski J, et al. Treadmill score quantifies electrocardiographic response to exercise and improves test accuracy and reproducibility. Circulation 1980; 61: 276–85

    Article  PubMed  CAS  Google Scholar 

  46. Hollenberg M, Wisneski J, Gertz E, et al. Computer derived treadmill exercise score quantifies the degree of revascularization and improved exercise performance after coronary artery bypass surgery. Am Heart J 1983; 106: 1096–104

    Article  PubMed  CAS  Google Scholar 

  47. Hollenberg M, Zoltick J, Go M, et al. Comparison of a quantitative treadmill exercise score with standard electrocardiographic criteria in screening asymptomatic young men for coronary artery disease. N Engl J Med 1985; 313: 600–6

    Article  PubMed  CAS  Google Scholar 

  48. Detrano R, Salcedo E, Leatherman J, et al. Computer assisted versus unassisted analysis of the exercise electrocardiogram in patients without myocardial infarction. J Am Coll Cardiol 1987; 10: 794–9

    Article  PubMed  CAS  Google Scholar 

  49. Vergari J, Hakki H, Heo J, et al. Merits and limitations of quantitative treadmill exercise score. Am Heart J 1987; 114: 819–26

    Article  PubMed  CAS  Google Scholar 

  50. Tateishi S, Abe S, Yamashita T, et al. Use of the QRS scoring system in the early estimation of myocardial infarct size following reperfusion. J Electrocardiol 1997; 30: 315–22

    Article  PubMed  CAS  Google Scholar 

  51. Birnbaum Y, Maynard C, Wolfe S, et al. Terminal QRS distortion on admission is better than ST-segment measurements in predicting final infarct size and assessing the potential effect of thrombolytic therapy in anterior wall acute myocardial infarction. Am J Cardiol 1999; 85: 530–4

    Article  Google Scholar 

  52. Sutter JD, Wiele CVd, Gheeraert P, et al. The Selvester 32-point QRS score for evaluation of myocardial infarct size after primary coronary angioplasty. Am J Cardiol 1999; 83: 255–7

    Article  PubMed  Google Scholar 

  53. Michaelides A, Ryan JP, Bacon JM, et al. Exercise induced QRS changes (Athens QRS score) in patients with coronary artery disease: a marker of myocardial ischemia. J Cardiol 1995; 26: 263–72

    PubMed  CAS  Google Scholar 

  54. Okin P, Kligfield P, Ameisen O, et al. Improved accuracy of exercise electrocardiogram: identification of three vessel coronary disease in stable angina pectoris by analysis of peak rate related changes in ST segments. Am J Cardiol 1985; 55: 271–6

    Article  PubMed  CAS  Google Scholar 

  55. Bobbio M, Detrano R. A lesson from the controversy about heart rate adjustment of ST segment depression. Circulation 1991; 84: 1410–3

    Article  PubMed  CAS  Google Scholar 

  56. Bobbio M, Detrano R, Schmid J, et al. Exercise-induced ST depression and ST/heart rate index to predict triple-vessel or left main coronary disease: a multicenter analysis. J Am Coll Cardiol 1992; 19: 11–8

    Article  PubMed  CAS  Google Scholar 

  57. Atwood J, Do D, Froelicher V. Can computerization of the exercise test replace the cardiologist? Am Heart J 1998; 136: 543–52

    Article  PubMed  CAS  Google Scholar 

  58. Stuart R, Ellestad M. Upsloping ST segments in exercise stress testing: six year follow up study of 438 patients and correlation with 248 angiograms. Am J Cardiol 1976; 37: 19–22

    Article  PubMed  CAS  Google Scholar 

  59. Gianrossi R, Detrano R, Mulvihill D, et al. Exercise induced ST depression in the diagnosis of coronary artery disease: a meta-analysis. Circulation 1989; 80: 87–98

    Article  PubMed  CAS  Google Scholar 

  60. Rijneke R, Ascoop C, Talmon J. Clinical significance of upsloping ST segments in exercise electrocardiography. Circulation 1980; 61: 671–8

    Article  PubMed  CAS  Google Scholar 

  61. Kurita A, Chaitman B, Bourassa M. Significance of exercise induced junctional ST depression in evaluation of coronary artery disease. Am J Cardiol 1977; 40: 492–7

    Article  PubMed  CAS  Google Scholar 

  62. Savvides M, Ahnve S, Bhargava V, et al. Computer analysis of exercise induced changes in electrocardiographic variables: comparison of methods and criteria. Chest 1983; 84: 699–706

    Article  PubMed  CAS  Google Scholar 

  63. Miranda C, Froelicher V, Froning J. Should ST amplitude be measured at ST0 or ST60? [abstract]. J Am Coll Cardiol 1991; 17: 192A

    Article  Google Scholar 

  64. Simoons ML, Hugenholtz PG. Estimation of the probability of exercise-induced ischemia by quantitative ECG analysis. Circulation 1977; 56: 552–9

    Article  PubMed  CAS  Google Scholar 

  65. Detry JM, Robert A, Luwaert RJ, et al. Diagnostic value of computerized exercise testing in men without previous myocardial infarction: a multivariate, compartmental and probabilistic approach. Eur Heart J 1985; 6: 227–38

    PubMed  CAS  Google Scholar 

  66. Pruvost P, Lablanche JM, Beuscart R, et al. Enhanced efficacy of computerized exercise test by multivariate analysis for the diagnosis of coronary artery disease: a study of 558 men without previous myocardial infarction. Eur Heart J 1987; 8: 1287–94

    PubMed  CAS  Google Scholar 

  67. Deckers JW, Rensing BJ, Tijssen JG, et al. A comparison of methods of analysing exercise tests for diagnosis of coronary artery disease. Br J Heart J 1989; 62: 438–44

    Article  CAS  Google Scholar 

  68. Froelicher VF, Lehmann KG, Thomas R, et al. The electrocardiographic exercise test in a population with reduced workup bias: diagnostic performance, computerized interpretation, and multivariable prediction. Veterans Affairs Cooperative Study in Health Services #016 Quantitative Exercise Testing and Angiography (QUEXTA) Study Group. Ann Intern Med 1998; 128: 965–74

    PubMed  CAS  Google Scholar 

  69. Detrano R, Salcedo E, Passalacqua M, et al. Exercise electrocardiographic variables: a critical appraisal. J Am Coll Cardiol 1986; 8: 836–47

    Article  PubMed  CAS  Google Scholar 

  70. Froelicher V. Educational cardiology page [online]. Available from: URL: http://www.cardiology.org [Accessed 2000 Aug 22]

    Google Scholar 

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Correspondence to Euan A. Ashley.

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Ashley, E.A., Froelicher, V.F. Computer Applications in the Interpretation of the Exercise Electrocardiogram. Sports Med 30, 231–248 (2000). https://doi.org/10.2165/00007256-200030040-00001

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