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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)

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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。

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References

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    MATH  Google Scholar 

  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

    Article  Google Scholar 

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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  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

    MATH  Google Scholar 

  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

    Article  Google Scholar 

  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

    Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China

    Cong Wang, Xunde Dong, Junmin Hu & Feifei Yang

  2. Department of Radiology, General Hospital of Guangzhou Military Command, Guangzhou, 510010, China

    Shanxing Ou

  3. Department 5 of Gerontology, General Hospital of Guangzhou Military Command, Guangzhou, 510010, China

    Wei Wang

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  1. Cong Wang
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  2. Xunde Dong
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Correspondence to Cong Wang.

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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

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