Evolutionary Optimization of ECG Feature Extraction Methods: Applications to the Monitoring of Adult Myocardial Ischemia and Neonatal Apnea Bradycardia Events

  • A. I. Hernández
  • J. Dumont
  • M. Altuve
  • A. Beuchée
  • G. Carrault
Chapter

Abstract

Although a significant bibliography exists on the application of signal processing methods to ECG signals, the optimal configuration of these methods so as to maximize their performance on clinical data is a complex problem that is seldom covered in the literature. This is particularly the case for the signal processing chains proposed for the detection and segmentation of individual beats, which are often characterized by a significant number or parameters (filter cut-off frequencies, thresholds, etc.). In this chapter we propose an automated method, based on evolutionary computing, to optimize these parameters in a joint fashion. A brief state of the art on current ECG segmentation methods is presented and a complete signal processing chain, adapted to the detection and segmentation of ECG signals is proposed. The evolutionary optimization method is described and applied to two different monitoring applications: the detection of myocardial ischemia episodes on adult patients and the characterization of apnea-bradycardia events on preterm infants.

References

  1. Altuve, M., Carrault, G., Cruz, J., Beuchée, A., Pladys, P., Hernández, A.: Analysis of the QRS complex for apnea-bradycardia characterization in preterm infants. In: Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pp. 946–949. IEEE, Minneapolis (2009)Google Scholar
  2. Back, T., Schutz, M.: Intelligent mutation rate control in canonical genetic algorithms: foundations of intelligent systems. In: Ras, Z., Michalewicz, M. (eds.) Proceedings of the International Symposium on Methodologies for Intelligent Systems, Lect. Notes Comput. Sci. 1079, 158–167. National Technical Information Service, Springer Berlin/Heidelberg (1996)Google Scholar
  3. Bahoura, M., Hassani, M., Hubin, M.: Dsp implementation of wavelet transform for real time ecg wave forms detection and heart analysis. Comput. Meth. Programs Biomed. 52, 35–44 (1997)CrossRefGoogle Scholar
  4. Beuchée, A.: Intérêt de l’analyse de la variabilité du rythme cardiaque en néonatalogie. comportement des systèmes de régulation cardiovasculaire dans le syndrome apnée/bradycardie du nouveau-né, vol. 1, PhD. thesis, Université de Rennes (2005)Google Scholar
  5. Castro, N., Gomis, P., Wagner, G.: Assessment of myocardial ischemia through high frequency energy estimation over the time-frequency plane using wavelets. Comput. Cardiol. 30, 517–520 (2004)Google Scholar
  6. Clavier, L., Boucher, J.: Segmentation of electrocardiograms using a hidden Markov model. In: Engineering in Medicine and Biology Society, 1996. Bridging Disciplines for Biomedicine. Proceedings of the 18th Annual International Conference of the IEEE, vol. 4, pp. 1409–1410. IEEE, Piscataway (1996)Google Scholar
  7. Coast, D.: Segmentation of high-resolution ECGs using hidden Markov models. Acoustic. Speech Signal Process. ICASSP-93 1, 67–70 (1993)Google Scholar
  8. Coast, D., Stern, R., Cano, G., Briller, S.: An approach to cardiac arrhythmia analysis using hidden Markov models. IEEE Trans. Biomed. Eng. 37(9), 826–836 (1990)CrossRefGoogle Scholar
  9. CSE: Recommandations for measurement standards in quantitative electrocardiography. Eur. Heart J.: The CSE Working Party 6 815–825 (1985)Google Scholar
  10. Doerschuck, P.: A Markov Chain Approach to Electrocardiogram Modeling and Analysis. PhD. thesis, Massachusetts Institute of Technology, Boston, Massachussetts, USA (1985)Google Scholar
  11. Dumont, J., Carrault, G., Gomis, P., Wagner, G., Hernandez, A.: Detection of myocardial ischemia with hidden Semi-Markovian models. In: Computers in Cardiology, 2009, pp. 121–124. IEEE, Park City (2010a)Google Scholar
  12. Dumont, J., Hernández, A.I., Carrault, G.: Improving ecg beats delineation with an evolutionary optimization process., IEEE Trans. Biomed. Eng. 57(3), 607–15 (2010b)Google Scholar
  13. Eichler West, R., de Schutter, E., Wilcox, G.: Using evolutionary algorithms to search for control parameters in a nonlinear partial differential equation. Ima Vol. Math. Appl. 111, 33–64 (1999)CrossRefGoogle Scholar
  14. Einthoven, W.: Galvanometrische registratie van het menschilijk electrocardiogram. In: Herinneringsbundel Professor SS Rosenstein, pp. 101–107. Eduard Ijdo, Leiden (1902)Google Scholar
  15. Garcia, J., Wagner, G., Sörnmo, L., Olmos, S., Lander, P., Laguna, P.: Temporal evolution of traditional versus transformed ECG-Based indexes in patients with induced myocardial ischemia. J. Electrocardiol. 33, 37–47 (2000)CrossRefGoogle Scholar
  16. Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K., Stanley, H.E.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)Google Scholar
  17. Graja, S., Boucher, J.: Multiscale hidden Markov model applied to ECG segmentation, In: IEEE International Symposium on Intelligent Signal Processing, 2003, pp. 105–109. IEEE Computer Society, Tokyo (2003)Google Scholar
  18. Graja, S., Boucher, S.: Hidden markov tree model applied to ECG delineation. IEEE Trans. Instrum. Meas. 54, 2163–2168 (2005)CrossRefGoogle Scholar
  19. Hernández, A.I., Carrault, G., Mora, F., Bardou, A.: Model-based interpretation of cardiac beats by evolutionary algorithms: signal and model interaction. Artif. Intell. Med. 26(3), 211–235 (2002)CrossRefGoogle Scholar
  20. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–72 (1992)CrossRefGoogle Scholar
  21. Houck, C., Joines, J., Kay, M.: A genetic algorithm for function optimization: A Matlab implementation, NCSU-IE TR 95-09 (1995)Google Scholar
  22. Hughes, N., Tarassenko, L., Roberts, S.: Markov models for automated ECG interval analysis. In: Proceedings NIPS, vol. 16, pp. 611–618. MIT Press, Cambridge, MA (2003)Google Scholar
  23. Islam, S., Jidin, R., Ali, M.: Performance study of adaptive filtering algorithms for noise cancellation of ECG signal. In: 7th International Conference on Information, Communications and Signal Processing, 2009. ICICS 2009, pp. 1–5. IEEE, Macau (2010)Google Scholar
  24. Jane, R., Laguna, P., Thakor, N., Caminal, P.: Adaptive baseline wander removal in the ECG: comparative analysis with cubic spline technique. Comput. Cardiol. 18, 143–146 (1992)CrossRefGoogle Scholar
  25. Jane, R., Blasi, A., Garcia, J., Laguna, P.: Evaluation of an automatic threshold based detector of waveform limits in Holter ECG with the QT database. In: Computers in Cardiology, pp. 295–298. IEEE Computer Society, Long Beach (1997)Google Scholar
  26. Janikow, C., Michalewicz, Z.: An experimental comparison of binary and floating point representations in genetic algorithms. In: Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 31–36. Morgan Kaufmann Publishers, San Mateo (1991)Google Scholar
  27. Kohler, B., Hennig, C., Orglmeister, R.: The principles of software QRS detection, IEEE Eng. Med. Biol. Mag. 21(1), 42–57 (2002)CrossRefGoogle Scholar
  28. Koski, A.: Modelling ECG signals with hidden Markov models. Artif. Intell. Med. 8(5), 453–471 (1996)CrossRefGoogle Scholar
  29. Laguna, P., Jane, R., Caminal, R.: Automatic detection of wave boundaries in multi-lead ecg signals: validation with the cse data-base. Comput. Biomed. Res. 27 45–60 (1994)CrossRefGoogle Scholar
  30. Langer, A., Armstrong, P.: ST segment monitoring in patients with acute ischemic syndromes: Past and future revue. J. Thromb. Thrombolysis 5, S119–S123 (1998)CrossRefGoogle Scholar
  31. Lepage, R., Boucher, J., Blanc, J., Cornilly, J.: ECG segmentation and P-wave feature extraction: application to patients prone to atrial fibrillation. In: Engineering in Medicine and Biology Society. Proceedings of the 23rd Annual International Conference of the IEEE, vol. 1, pp. 298–301 Istanbul (2001)Google Scholar
  32. Leski, J., Henzel, N.: ECG baseline wander and powerline interference reduction using nonlinear filter bank. Signal Process. 85(4), (2005) 781–793MATHCrossRefGoogle Scholar
  33. Levkov, C., Mihov, G., Ivanov, R., Daskalov, I., Ivaylo, C., Dotsinsky, I.: Removal of power-line interference from the ECG: a review of the subtraction procedure. Biomed. Eng. Online 4, 50 (2005)CrossRefGoogle Scholar
  34. Li, C., Zheng, C., Tai, C.: Detection of ECG characteristic points using wavelet transforms. IEEE Trans. Biomed. Eng. 42(1), 21–28 (1995)CrossRefGoogle Scholar
  35. Martinez, J.P., Almeida, R., Olmos, S., Rocha, A.P., Laguna, P.: A wavelet-based ecg delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51, 570–581 (2004)CrossRefGoogle Scholar
  36. Martinez, J., Olmos, S., Wagner, G., Laguna, P.: Characterization of repolarization alternans during ischemia: time-course and spatial analysis. IEEE Trans. Biomed. Eng. 53, 701–11 (2006)CrossRefGoogle Scholar
  37. McGinn, A., Rosamond, W., Goff, D., et al.: Trends in prehospital delay time and use of emergency medical services for acute myocardial infarction: experience in 4 US communities from 1987–2000. Am. Heart J. 150(3), 392–400 (2005)CrossRefGoogle Scholar
  38. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Berlin/Heidelberg (1996)MATHGoogle Scholar
  39. Mneimneh, M., Yaz, E., Johnson, M., Povinelli, R.: An adaptive Kalman filter for removing baseline wandering in ECG signals. In: Computers in Cardiology, 2006, pp. 253–256. IEEE, Valencia (2008)Google Scholar
  40. Pan, J., Tompkins, W.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32 230–236 (1985)CrossRefGoogle Scholar
  41. Penzel, T., Nottrott, M., Canisius, S., Greulich, T., Becker, H.F., Vogelmeier, C. P428 ecg morphology changes improves detection of obstructive sleep apnea. Sleep Med. 7(Suppl 2), S101–S102 (2006)CrossRefGoogle Scholar
  42. Pettersson, J., Pahlm, O., Carro, E., Edenbrandt, L., Ringborn, M., Sörnmo, L., Warren, S.G., Wagner, G.S.: Changes in high-frequency QRS components are more sensitive than ST-segment deviation for detecting acute coronary artery occlusion. J. Am. Coll. Cardiol. 36, 1827–1834 (2000)CrossRefGoogle Scholar
  43. Poets, C.F., Stebbens, V.A., Samuels, M.P., Southall, D.P.: The relationship between bradycardia, apnea, and hypoxemia in preterm infants. Pediatr. Res. 34(2), 144–147 (1993)CrossRefGoogle Scholar
  44. Portet, F., Hernandez, A., Carrault, G., Evaluation of real-time QRS detection algorithms in variable contexts. Med. Biol. Eng. Comput. 43(3), 379–385 (2005)CrossRefGoogle Scholar
  45. Pueyo, E., Sörnmo, L., Laguna, P.: QRS slopes for detection and characterization of myocardial ischemia. IEEE Trans. Biomed. Eng. 55, 468–477 (2008)CrossRefGoogle Scholar
  46. Sörnmo, L., Laguna, P.: Bioelectrical Signal Processing in Cardiac and Neurological Applications. Elsevier Academic Press, Amsterdam/Boston (2005)Google Scholar
  47. Sebag, M., Schoenauer, M., Ravise, C.: Toward civilized evolution: developing inhibitions. In: Bäck, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 291–298. Morgan Kaufmann, San Francisco (1997)Google Scholar
  48. Senhadji, L., Carrault, G., Bellanger, J., Passariello, G.: Comparing wavelet transforms for recognizing cardiac patterns. Eng. Med. Biol. Mag. IEEE 14(2), 167–173 (2002)CrossRefGoogle Scholar
  49. Shusterman, V., Shah, S.I., Beigel, A., Anderson, K.P.: Enhancing the precision of ECG baseline correction: selective filtering and removal of residual error. Comput. Biomed. Res. 33(2), 144–160 (2000)CrossRefGoogle Scholar
  50. Smrdel, A., Jager, F.: Automated detection of transient ST-segment episodes in 24h electrocardiograms. Med. Biol. Eng. Comput. 42(3), 303–311 (2004)CrossRefGoogle Scholar
  51. Soria-Olivas, E., Martínez-Sober, M., Calpe-Maravilla, J., Guerrero-Martínez, J.F., Chorro-Gascó, J., Espí-López, J.: Application of adaptive signal processing for determining the limits of P and T waves in an ECG. IEEE Trans. Biomed. Eng. 45(8), 1077–1080 (1998)CrossRefGoogle Scholar
  52. Thakor, N., Zhu, Y.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Biomed. Eng. 38(8), 785–794 (2002)CrossRefGoogle Scholar
  53. Thierens, D.: Adaptive mutation rate control schemes in genetic algorithms. In: Proceedings of the 2002 Congress on Evolutionary Computation, vol. 1, pp. 980–985. Barcelona (2002)Google Scholar
  54. Thoraval, L.: Analyse statistique de signaux électrocardiographiques par modèles de markov cachés. PhD. thesis, Université de Rennes (1995)Google Scholar
  55. Touzé, E., Varenne, O., Chatellier, G., Peyrard, S., Rothwell, P., Mas, J.-L.: Risk of myocardial infarction and vascular death after transient ischemic attack and ischemic stroke: a systematic review and meta-analysis. Stroke 36, 2748 (2005)CrossRefGoogle Scholar
  56. van Ravenswaaij-Arts, C., Kollee, L., Hopman, J., Stoelinga, G., van Geijn, H.: Heart rate variability. Ann. Intern. Med. 118(6), 436 (1993)Google Scholar
  57. Vullings, H., Verhaegen, M., Verbruggen, H.: Automated ECG segmentation with dynamic time warping. In: Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, vol. 1, pp. 163–166. IEEE Service Center, Picatway (1998)Google Scholar
  58. Yang, L., Zhang, S., Li, X., Yang, Y.: Removal of pulse waveform baseline drift using cubic spline interpolation. In: 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), 2010, pp. 1–3. IEEE, Shenzhen (2010)Google Scholar
  59. Ziarani, A.K., Konrad, A.: A nonlinear adaptive method of elimination of power line interference in ECG signals. IEEE Trans. Biomed. Eng. 49(6), 540–547 (2002)CrossRefGoogle Scholar
  60. Zifan, A., Saberi, S., Moradi, M.H., Towhidkhah, F.: Automated ECG segmentation using piecewise derivative dynamic time warping. Int. J. Biomed. Sci. 1(3), 181–185 (2006)Google Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • A. I. Hernández
    • 1
    • 2
  • J. Dumont
    • 1
    • 2
    • 3
  • M. Altuve
    • 1
    • 2
    • 4
  • A. Beuchée
    • 1
    • 2
    • 5
  • G. Carrault
    • 1
    • 2
  1. 1.INSERM, U642RennesFrance
  2. 2.Université de Rennes 1, LTSIRennesFrance
  3. 3.SORIN Group CRMClamartFrance
  4. 4.Department of Industrial TechnologySimon Bolivar UniversityCaracasVenezuela
  5. 5.Département de PédiatriePavillon Le Chartier, CHURennesFrance

Personalised recommendations