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Quantification of Diagnostic Information from Electrocardiogram Signal: A Review

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Advances in Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 347))

Abstract

Electrocardiogram (ECG) contains the information about the contraction and relaxation of heart chambers. This diagnostic information will change due to various cardiovascular diseases. This information is used by a cardiologist for accurate detection of various life-threatening cardiac disorders. ECG signals are subjected to number of processing, for computer aided detection and localization of cardiovascular diseases. These processing schemes are categorized as filtering, synthesis, compression and transmission. Quantifying diagnostic information from an ECG signal in an efficient way, is always a challenging task in the area of signal processing. This paper presents a review on state-of-art diagnostic information extraction approaches and their applications in various ECG signal processing schemes such as quality assessment and cardiac disease detection. Then, a new diagnostic measure for multilead ECG (MECG) is proposed. The proposed diagnostic measure (MSD) is defined as the difference between multivariate sample entropy values for original and processed MECG signals. The MSD measure is evaluated over MECG compression framework. Experiments are conducted over both normal and pathological MECG from PTB database. The results demonstrate that the proposed MSD measure is effective in quantifying diagnostic information in MECG. The MSD measure is also compare with other measures such as WEDD, PRD and RMSE.

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References

  1. Opie, L.H.: Heart Physiology: From Cell to Circulation. Lippincott Williams & Wilkins, Philadelphia (2004)

    Google Scholar 

  2. Goldberger, A.L.: Clinical Electrocardiography: A Simplified Approach. Elsevier Health Sciences, Philadelphia (2012)

    Google Scholar 

  3. De Luna, A.B.: Clinical Electrocardiography: A Textbook. Wiley, Chichester (2012)

    Book  Google Scholar 

  4. Sundnes, J., Lines, G.T., Cai, X., Nielsen, B.F., Mardal, K.-A., Tveito, A.: Computing the Electrical Activity in the Heart, vol. 1. Springer, New York (2007)

    Google Scholar 

  5. McSharry, P.E., Clifford, G.D., Tarassenko, L., Smith, L.A.: A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50(3), 289–294 (2003)

    Article  Google Scholar 

  6. Malmivuo, J., Plonsey, R.: Bioelectromagnetism: Principles and Applications of Bioelectric and Biomagnetic Fields. Oxford University Press, New York (1995)

    Book  Google Scholar 

  7. Fereniec, M., Maniewski, R., Karpinski, G., Opolski, G., Rix, H.: High-resolution multichannel measurement and analysis of cardiac repolarization. Biocybern. Biomed. Eng. 28(3), 61–69 (2008)

    Google Scholar 

  8. Thaler, M.S.: The Only EKG Book You’ll Ever Need, vol. 365. Lippincott Williams & Wilkins, Philadelphia (2010)

    Google Scholar 

  9. Manikandan, M.S., Dandapat, S.: Wavelet energy based diagnostic distortion measure for ECG. Biomed. Signal Process. Control 2(2), 80–96 (2007)

    Article  Google Scholar 

  10. Bradie, B.: Wavelet packet-based compression of single lead ECG. IEEE Trans. Biomed. Eng. 43(5), 493–501 (1996)

    Article  Google Scholar 

  11. de Filho, E.B.L., da Silva, E.A.B., de Carvalho, M.B., da Silva Jr, W.S., Koiller, J.: Electrocardiographic signal compression using multiscale recurrent patterns. IEEE Trans. Circuits Syst. I: Regul. Pap. 52(12), 2739–2753 (2005)

    Article  Google Scholar 

  12. Miaou, S.-G., Yen, H.-L.: Quality driven gold washing adaptive vector quantization and its application to ECG data compression. IEEE Trans. Biomed. Eng. 47(2), 209–218 (2000)

    Article  Google Scholar 

  13. Tai, S.C.: ECG data compression by corner detection. Med. Biol. Eng. Comput. 30(6), 584–590 (1992)

    Article  MathSciNet  Google Scholar 

  14. Chen, J., Itoh, S.: A wavelet transform-based ECG compression method guaranteeing desired signal quality. IEEE Trans. Biomed. Eng. 45(12), 1414–1419 (1998)

    Article  Google Scholar 

  15. D’Ambrosio, A.C., Ortiz-Conde, A., Sanchez, E.J.G.: Percentage area difference (PAD) as a measure of distortion and its use in maximum enclosed area (MEA), a new ECG signal compression algorithm. In: Proceedings of the Fourth IEEE International Caracas Conference on Devices, Circuits and Systems, pp. I035-1–I035-5 (2002)

    Google Scholar 

  16. Chou, H.-H., Chen, Y.-J., Shiau, Y.-C., Kuo, T.-S.: An effective and efficient compression algorithm for ECG signals with irregular periods. IEEE Trans. Biomed. Eng. 53(6), 1198–1205 (2006)

    Article  Google Scholar 

  17. Zigel, Y., Cohen, A., Katz, A.: The weighted diagnostic distortion (WDD) measure for ECG signal compression. IEEE Trans. Biomed. Eng. 47(11), 1422–1430 (2000)

    Article  Google Scholar 

  18. Al-Fahoum, A.S.: Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure. IEEE Trans. Inf. Technol. Biomed. 10(1), 182–191 (2006)

    Article  Google Scholar 

  19. Manikandan, M.S., Dandapat, S.: Multiscale entropy-based weighted distortion measure for ECG coding. IEEE Signal Process. Lett. 15, 829–832 (2008)

    Article  Google Scholar 

  20. Manikandan, M.S., Dandapat, S.: Effective quality-controlled SPIHT-based ECG coding strategy under noise environments. Electron. Lett. 44(20), 1182–1183 (2008)

    Article  Google Scholar 

  21. Pecchia, L., Melillo, P., Bracale, M.: Remote health monitoring of heart failure with data mining via CART method on HRV features. IEEE Trans. Biomed. Eng. 58(3), 800–804 (2011)

    Article  Google Scholar 

  22. Acharya, U.R., Faust, O., Sree, S.V., Ghista, D.N., Dua, S., Joseph, P., Ahamed, V.I.T., Janarthanan, N., Tamura, T.: An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes. Comput. Methods Biomech. Biomed. Eng. 16(2), 222–234 (2013)

    Article  Google Scholar 

  23. Ramirez-Villegas, J.F., Lam-Espinosa, E., Ramirez-Moreno, D.F., Calvo-Echeverry, P.C., Agredo-Rodriguez, W.: Heart rate variability dynamics for the prognosis of cardiovascular risk. PLoS ONE 6(2), e17060 (2011)

    Article  Google Scholar 

  24. Schmitt, L., Regnard, J., Desmarets, M., Mauny, F., Mourot, L., Fouillot, J.-P., Coulmy, N., Millet, G.: Fatigue shifts and scatters heart rate variability in elite endurance athletes. PLoS ONE 8(8), e71588 (2013)

    Article  Google Scholar 

  25. Chouchou, F., Pichot, V., Barthélémy, J.-C., Bastuji, H., Roche, F.: Cardiac sympathetic modulation in response to apneas/hypopneas through heart rate variability analysis. PLoS ONE 9(1), e86434 (2014)

    Article  Google Scholar 

  26. Banerjee, S., Mitra, M.: Application of cross wavelet transform for ECG pattern analysis and classification. IEEE Trans. Instrum. Meas. 63(2), 326–333 (2014)

    Article  Google Scholar 

  27. Jayachandran, E.S., et al.: Analysis of myocardial infarction using discrete wavelet transform. J. Med. Syst. 34(6), 985–992 (2010)

    Article  Google Scholar 

  28. Ge, D., Srinivasan, N., Krishnan, S.M.: Cardiac arrhythmia classification using autoregressive modeling. Biomed. Eng. Online 1(1), 5 (2002)

    Article  Google Scholar 

  29. Martis, R.J., Acharya, U.R., Lim, C.M., Suri, J.S.: Characterization of ECG beats from cardiac arrhythmia using discrete cosine transform in PCA framework. Knowl.-Based Syst. 45, 76–82 (2013)

    Article  Google Scholar 

  30. Langley, P., Bowers, E.J., Murray, A.: Principal component analysis as a tool for analyzing beat-to-beat changes in ECG features: application to ECG-derived respiration. IEEE Trans. Biomed. Eng. 57(4), 821–829 (2010)

    Article  Google Scholar 

  31. Widjaja, D., Varon, C., Dorado, A.C., Suykens, J.A.K., Van Huffel, S.: Application of kernel principal component analysis for single-lead-ECG-derived respiration. IEEE Trans. Biomed. Eng. 59(4), 1169–1176 (2012)

    Article  Google Scholar 

  32. Martis, R.J., Acharya, U.R., Min, L.C.: ECG beat classification using PCA, LDA, ICA and discrete wavelet transform. Biomed. Signal Process. Control 8(5), 437–448 (2013)

    Article  Google Scholar 

  33. Giri, D., Acharya, U.R., Martis, R.J., Sree, S.V., Lim, T.-C., Ahamed, T., Suri, J.S.: Automated diagnosis of coronary artery disease affected patients using LDA, PCA, ICA and discrete wavelet transform. Knowl.-Based Syst. 37, 274–282 (2013)

    Article  Google Scholar 

  34. Sun, L., Yanping, L., Yang, K., Li, S.: ECG analysis using multiple instance learning for myocardial infarction detection. IEEE Trans. Biomed. Eng. 59(12), 3348–3356 (2012)

    Article  Google Scholar 

  35. Pan, J., Tompkins, W.J.: A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)

    Article  Google Scholar 

  36. Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol.-Heart Circ. Physiol. 278(6), H2039–H2049 (2000)

    Google Scholar 

  37. Alonso-Atienza, F., Morgado, E., Fernandez-Martinez, L., Garcia-Alberola, A., Rojo-Alvarez, J.L.: Detection of life-threatening arrhythmias using feature selection and support vector machines. IEEE Trans. Biomed. Eng. 61(3), 832–840 (2014)

    Article  Google Scholar 

  38. Li, Q., Rajagopalan, C., Clifford, G.D.: Ventricular fibrillation and tachycardia classification using a machine learning approach. IEEE Trans. Biomed. Eng. 61(6), 1607–1613 (2014)

    Article  Google Scholar 

  39. Huang, J.-R., Fan, S.-Z., Abbod, M.F., Jen, K.-K., Wu, J.-F., Shieh, J.-S.: Application of multivariate empirical mode decomposition and sample entropy in EEG signals via artificial neural networks for interpreting depth of anesthesia. Entropy 15(9), 3325–3339 (2013)

    Article  MathSciNet  Google Scholar 

  40. Sharma, L.N., Dandapat, S., Mahanta, A.: Multichannel ECG data compression based on multiscale principal component analysis. IEEE Trans. Inf. Technol. Biomed. 16(4), 730–736 (2012)

    Article  Google Scholar 

  41. Fleureau, J., Kachenoura, A., Albera, L., Nunes, J.-C., Senhadji, L.: Multivariate empirical mode decomposition and application to multichannel filtering. Signal Process. 91(12), 2783–2792 (2011)

    Article  Google Scholar 

  42. Sharma, L.N., Dandapat, S.: Compressed sensing for multi-lead electrocardiogram signals. In: 2012 World Congress on Information and Communication Technologies (WICT), pp. 812–816, October 2012

    Google Scholar 

  43. Cetin, A.E., Koymen, H., Aydin, M.C.: Multichannel ECG data compression by multirate signal processing and transform domain coding techniques. IEEE Trans. Biomed. Eng. 40(5), 495–499 (1993)

    Article  Google Scholar 

  44. Ahmed, M.U., Mandic, D.P.: Multivariate multiscale entropy analysis. IEEE Signal Process. Lett. 19(2), 91–94 (2012)

    Article  Google Scholar 

  45. Alcaraz, R., Rieta, J.J.: A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms. Biomed. Signal Process. Control 5(1), 1–14 (2010)

    Article  Google Scholar 

  46. Oeff, M., Koch, H., Bousseljot, R., Kreiseler, D.: The PTB diagnostic ECG database. National Metrology Institute of Germany. http://www.physionet.org/physiobank/database/ptbdb (2012)

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Authors and Affiliations

  1. Indian Institute of Technology Guwahati, Guwahati, 781039, India

    S. Dandapat, L. N. Sharma & R. K. Tripathy

Authors
  1. S. Dandapat
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  2. L. N. Sharma
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  3. R. K. Tripathy
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Corresponding author

Correspondence to S. Dandapat .

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Editors and Affiliations

  1. Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Prabin Kumar Bora

  2. Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    S R Mahadeva Prasanna

  3. Electronics and Communication Technology, Gauhati University, Guwahati, Assam, India

    Kandarpa Kumar Sarma

  4. Electronics & Telecommunication Engg, Assam Engineering College, Guwahati, Assam, India

    Navajit Saikia

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Dandapat, S., Sharma, L.N., Tripathy, R.K. (2015). Quantification of Diagnostic Information from Electrocardiogram Signal: A Review. In: Bora, P., Prasanna, S., Sarma, K., Saikia, N. (eds) Advances in Communication and Computing. Lecture Notes in Electrical Engineering, vol 347. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2464-8_2

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  • DOI: https://doi.org/10.1007/978-81-322-2464-8_2

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2463-1

  • Online ISBN: 978-81-322-2464-8

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