Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG)

一种心肌缺血早期检测新方法:心电动力学图(cardiodynamicsgram)

  • 187 Accesses

  • 9 Citations

Abstract

Early detection of myocardial ischemia via electrocardiographic methods is important and challenging. In the study, based on the standard 12-lead electrocardiography (ECG), a new method called cardiodynamicsgram (CDG) is proposed for early detection of myocardial ischemia. Using a recently proposed deterministic learning algorithm, the cardiodynamics information is extracted from the ST-T segments of standard 12-lead ECG. The CDG is generated by plotting the three-dimensional cardiodynamics information. By analyzing CDG morphology, it is found that significant correlations exist between CDG and ischemia. By evaluating ischemia patients and healthy controls from the Physikalisch-Technische Bundesanstalt (PTB) database and the General Hospital of Guangzhou Military Command, the CDG method achieves a mean sensitivity of 90.3% and a mean specificity of 87.8%, which are higher than those of both the standard 12-lead ECG and the exercise ECG. As it is noninvasive, convenient, and inexpensive, it is hopeful that CDG may become a cost-effective screening method for early detection of ischemic heart diseases.

创新点

基于心电图对心肌缺血/冠心病进行早期检测是一个重要且具有挑战性的问题。在本文中,我们提出了一种心肌缺血早期检测新方法——心电动力学图 (cardiodynamicsgram, CDG). 该方法基于确定学习理论, 将心电图ST段及T波的心动力学信息提取出来, 并将其可视化显示。分析发现,心电动力学图(CDG)的形态与心肌缺血之间存在重要关联。临床实验表明,与常规12导联心电图和平板运动心电图相比,心电动力学图对心肌缺血的检测更为准确有效。此外,该方法无创、经济、方便, 有望成为一种心肌缺血/冠心病早期检测手段。

This is a preview of subscription content, log in to check access.

References

  1. 1

    Roger V L, Go A S, Lloyd-Jones D M, et al. Heart disease and stroke statistics 2011 update: a report from the American Heart Association. Circulation, 2011, 123: e18–e209

  2. 2

    Lahsasna A, Ainon R A, Zainuddin R, et al. Design of a fuzzy-based decision support system for coronary heart disease diagnosis. J Med Syst, 2012, 36: 3293–3306

  3. 3

    Drew B J, Pelter M M, Lee E, et al. Designing prehospital ECG systems for acute coronary syndromes. Lessons learned from clinical trials involving 12-lead ST-segment monitoring. J Electrocardiol, 2005, 38: 180–185

  4. 4

    Detrano R, Gianrossi R, Froelicher V. The diagnostic accuracy of the exercise electrocardiogram: a meta-analysis of 22 years of research. Prog Cardiovasc Dis, 1989, 32: 173–206

  5. 5

    Mora S, Redberg R F, Cui Y D, et al. Ability of exercise testing to predict cardiovascular and all-cause death in asymptomatic women: a 20-year follow-up of the lipid research clinics prevalence study. J Am Med Assoc, 2003, 290: 1600–1607

  6. 6

    Huebner T, Goernig M, Schuepbach M, et al. Electrocardiologic and related methods of non-invasive detection and risk stratification in myocardial ischemia: state of the art and perspectives. Ger Med Sci, 2010, 8: Doc27

  7. 7

    Frank E. An accurate, clinically practical system for spatial vectorcardiography. Circulation, 1956, 13: 737–749

  8. 8

    Tatsumi H, Takagi M, Nakagawa E, et al. Risk stratification in patients with Brugada syndrome: analysis of daily fluctuations in 12-lead electrocardiogram (ECG) and signal-averaged electrocardiogram (SAECG). J Cardiovasc Electr, 2006, 7: 705–711

  9. 9

    Kuchar D L, Thorburn C W, Sammel N L. Prediction of serious arrhythmic events after myocardial infarction: signalaveraged electrocardiogram, Holter monitoring and radionuclide ventriculography. J Am Coll Cardiol, 1987, 9: 531–538

  10. 10

    Medvegy M, Duray G, Pinter A, et al. Body surface potential mapping: historical background, present possibilities, diagnostic challenges. Ann Noninvas Electro, 2002, 7: 139–151

  11. 11

    Simonyi G. Electrocardiological features in obesity: the benefits of body surface potential mapping. Cardiorenal Med, 2014, 4: 123–129

  12. 12

    Sanz E, Steger J P, Thie W. Cardiogoniometry. Clin Cardiol, 1983, 6: 199–206

  13. 13

    Huebner T, Schuepbach W M, Seeck A, et al. Cardiogoniometric parameters for detection of coronary artery disease at rest as a function of stenosis localization and distribution. Med Biol Eng Comput, 2010, 48: 435–446

  14. 14

    Demidova M M, Martin-Yebra A, Martinez J P, et al. T wave alternans in experimental myocardial infarction: time course and predictive value for the assessment of myocardial damage. J Electrocardiol, 2013, 46: 263–269

  15. 15

    Mollo R, Cosenza A, Spinelli A, et al. T-wave alternans in apparently healthy subjects and in different subsets of patients with ischaemic heart disease. Europace, 2012, 14: 272–277

  16. 16

    Minchole A, Skarp B, Jager F, et al. Evaluation of a root mean squared based ischemia detector on the long-term ST database with body position change cancelation. Comput Cardiol, 2005, 32: 853–856

  17. 17

    Stadler R, Lu S, Nelson S, et al. A real-time ST-segment monitoring algorithm for implantable devices. J Electrocardiol, 2011, 34: 119–126

  18. 18

    Garcia J, Sornmo L, Olmos S, et al. Automatic detection of ST-T complex changes on the ECG using filtered RMS difference series: application to ambulatory ischemia monitoring. IEEE Trans Biomed Eng, 2000, 47: 1195–1201

  19. 19

    Smrdel A, Jager F. Automated detection of transient ST-segment episodes in 24h electrocardiograms. Med Biol Eng Comput, 2004, 42: 303–311

  20. 20

    Maglaveras N, Stamkopoulos T, Pappas C, et al. An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database. IEEE Trans Biomed Eng, 1998, 45: 805–813

  21. 21

    Papaloukas C, Fotiadis D I, Likas A, et al. An ischemia detection method based on artificial neural networks. Artif Intell Med, 2002, 24: 167–178

  22. 22

    Afsar F A, Arif M, Yang J. Detection of ST segment deviation episodes in ECG using KLT with an ensemble neural classifier. Physiol Meas, 2008, 29: 747–760

  23. 23

    Exarchos T P, Tsipouras M G, Exarchos C P, et al. A methodology for the automated creation of fuzzy expert systems for ischaemic and arrhythmic beat classification based on a set of rules obtained by a decision tree. Artif Intell Med, 2007, 40: 187–200

  24. 24

    Dranca L, Goni A, Illarramendi A. Real-time detection of transient cardiac ischemic episodes from ECG signals. Physiol Meas, 2009, 30: 983–998

  25. 25

    Wang C, Chen T R. Rapid detection of small oscillation faults via deterministic learning. IEEE Trans Neural Netw, 2011, 22: 1284–1296

  26. 26

    Goldberger A L, Amaral L, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101: e215–e220

  27. 27

    Hong P, Huang T S. Automatic temporal pattern extraction and association. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, 2002. II-2005–II-2008

  28. 28

    Wang D L. Temporal pattern processing. In: Arbib M A, ed. The Handbook of Brain Theory and Neural Networks. Cambridge: MIT Press, 2003. 1163–1167

  29. 29

    Wang C, Hill D J. Learning from neural control. IEEE Trans Neural Netw, 2006, 17: 130–146

  30. 30

    Wang C, Hill D J. Deterministic learning and rapid dynamical pattern recognition. IEEE Trans Neural Netw, 2007, 18: 617–630

  31. 31

    Wang C, Hill D J. Deterministic Learning Theory for Identification, Recognition, and Control. Boca Raton: CRC Press, 2009. 37–59

  32. 32

    Haykin S. Neural Networks: a Comprehensive Foundation. 2nd ed. Upper Saddle River: Prentice-Hall, 1999. 256–312

  33. 33

    Dower G E, Machado H B. XYZ data interpreted by a 12-lead computer program using the derived electrocardiogram. J Electrocardiol, 1979, 12: 249–261

  34. 34

    Dower G E, Machado H B, Osborne J A. On deriving the electrocardiogram from vectorcardiographic leads. Clin Cardiol, 1980, 3: 87–95

  35. 35

    Dower G E, Yakush A, Nazzal S B, et al. Deriving the 12-lead electrocardiogram from four (EASI) electrodes. J Electrocardiol, 1988, 21: S182–S187

  36. 36

    Kors J, van Herpen G, Sittig A, et al. Reconstruction of the frank vectorcardiogram from standard electrocardiographic leads: diagnostic comparison of different methods. Eur Heart J, 1990, 11: 1083–1092

  37. 37

    Wang C, Chen T R, Liu T F. Deterministic learning and data-based modeling and control. Acta Automat Sin, 2009, 35: 693–706

  38. 38

    Yuan C Z, Wang C. Design and performance analysis of deterministic learning of sampled-data nonlinear systems. Sci China Inf Sci, 2014, 57: 032201

  39. 39

    Song J, Yan H, Xiao Z, et al. A robust and efficient algorithm for ST-T complex detection in electrocardiograms. J Mech Med Biol, 2011, 11: 1103–1111

  40. 40

    McConahay D R, Mc Callister B D, Hallermann F J, et al. Comparative quantitative analysis of the electrocardiogram and the vectorcardiogram. Correlations with the coronary arteriogram. Circulation, 1970, 42: 245–259

  41. 41

    Mehta J, Hoffman I, Smedresman P, et al. Vectorcardiographic, electrocardiographic, and angiographic correlations in apparently isolated inferior wall myocardial infarction. Am Heart J, 1976, 91: 699–704

  42. 42

    Murray R G, Lorimer A R, Dunn F G, et al. Comparison of 12-lead and computer-analysed 3 orthogonal lead electocardiogram in coronary artery disease. Br Heart J, 1976, 38: 773–778

  43. 43

    Tatum J L, Jesse R L, Kontos M C, et al. Comprehensive strategy for the evaluation and triage of the chest pain patient. Ann Emerg Med, 1997, 29: 116–125

  44. 44

    Jesse R L, Kontos M C. Evaluation of chest pain in the emergency department. Curr Probl Cardiol, 1997, 22: 149–236

  45. 45

    Sinha M K, Roy D, Gaze D C, et al. Role of “ischemia modified albumin”, a new biochemical marker of myocardial ischaemia, in the early diagnosis of acute coronary syndromes. Emerg Med J, 2004, 21: 29–34

  46. 46

    Dangas G, Mehran R, Wallenstein S, et al. Correlation of angiographic morphology and clinical presentation in unstable angina. J Am Coll Cardiol, 1997, 29: 519–525

  47. 47

    Donohue T J, Kern M J, Aguirre F V, et al. Assessing the hemodynamic significance of coronary artery stenoses: analysis of translational pressure-flow velocity relations in patients. J Am Coll Cardiol, 1993, 22: 449–458

  48. 48

    Sanidas E, Dangas G. Evolution of intravascular assessment of coronary anatomy and physiology: from ultrasound imaging to optical and flow assessment. Eur J Clin Invest, 2013, 43: 996–1008

  49. 49

    Almeda F Q, Kason T T, Nathan S, et al. Silent myocardial ischemia: concepts and controversies. Am J Med, 2004, 116: 112–118

Download references

Author information

Correspondence to Cong Wang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Dong, X., Ou, S. et al. A new method for early detection of myocardial ischemia: cardiodynamicsgram (CDG). Sci. China Inf. Sci. 59, 1–11 (2016). https://doi.org/10.1007/s11432-015-5309-7

Download citation

Keywords

  • early detection
  • myocardial ischemia
  • cardiodynamicsgram (CDG)
  • ECG
  • deterministic learning
  • cardiodynamics
  • 012104

关键词

  • 早期检测
  • 心肌缺血
  • 心电动力学图
  • ECG
  • 确定学习
  • 冠心病