Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2016, Article ID 1675785, 11 pages
http://dx.doi.org/10.1155/2016/1675785
Research Article

The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart Failure

1Universidade CEUMA, No. 100, 65903-093 Imperatriz, MA, Brazil
2Laboratory for Biological Information Processing, Universidade Federal do Maranhão, S/N, São Luís, MA, Brazil
3Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi 464-8603, Japan

Received 2 March 2016; Revised 13 June 2016; Accepted 26 July 2016

Academic Editor: Said Audi

Copyright © 2016 Fausto Lucena et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Neubauer, “The failing heart—an engine out of fuel,” The New England Journal of Medicine, vol. 356, no. 11, pp. 1140–1151, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. R. E. Lane, M. R. Cowie, and A. W. C. Chow, “Prediction and prevention of sudden cardiac death in heart failure,” Heart, vol. 91, no. 5, pp. 674–680, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Najafi, A. J. Dobson, and K. Jamrozik, “Is mortality from heart failure increasing in Australia? An analysis of official data on mortality for 1997–2003,” Bulletin of the World Health Organization, vol. 84, no. 9, pp. 722–728, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. N. A. M. Estes III and D. Denofrio, “The challenge of prediction and prevention of sudden cardiac death in congestive heart failure,” Journal of Interventional Cardiac Electrophysiology, vol. 5, no. 1, pp. 5–8, 2001. View at Publisher · View at Google Scholar · View at Scopus
  5. M. W. Rich and R. F. Nease, “Cost-effectiveness analysis in clinical practice: the case of heart failure,” Archives of Internal Medicine, vol. 159, no. 15, pp. 1690–1700, 1999. View at Publisher · View at Google Scholar · View at Scopus
  6. J. D. Piette, K. C. Lun, L. A. Moura Jr. et al., “Impacts of e-health on the outcomes of care in low- and middle-income countries: where do we go from here?” Bulletin of the World Health Organization, vol. 90, no. 5, pp. 365–372, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. F. Shamsham and J. Mitchell, “Essentials of the diagnosis of heart failure,” American Family Physician, vol. 61, no. 5, pp. 1319–1328, 2000. View at Google Scholar · View at Scopus
  8. C. Fonseca, T. Mota, H. Morais et al., “The value of the electrocardiogram and chest X-ray for confirming or refuting a suspected diagnosis of heart failure in the community,” European Journal of Heart Failure, vol. 6, no. 6, pp. 807–812, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. S. J. Hutchison, Principles of Echocardiography and Intracardiac Echocardiography: Expert Consult, Saunders, Philadelphia, Pa, USA, 2012.
  10. J. Remes, H. Miettinen, A. Reunanen, and K. Pyörälä, “Validity of clinical diagnosis of heart failure in primary health care,” European Heart Journal, vol. 12, no. 3, pp. 315–321, 1991. View at Google Scholar · View at Scopus
  11. Y. Işler and M. Kuntalp, “Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure,” Computers in Biology and Medicine, vol. 37, no. 10, pp. 1502–1510, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. Y. Işler and M. Kuntalp, “Heart rate normalization in the analysis of heart rate variability in congestive heart failure,” Proceedings of the Institution of Mechanical Engineers, vol. 224, no. 3, pp. 453–463, 2010. View at Google Scholar
  13. A. Kampouraki, G. Manis, and C. Nikou, “Heartbeat time series classification with support vector machines,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 4, pp. 512–518, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. E. D. Übeyli, “ECG beats classification using multiclass support vector machines with error correcting output codes,” Digital Signal Processing, vol. 17, no. 3, pp. 675–684, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. R. A. Thuraisingham, “A classification system to detect congestive heart failure using second-order difference plot of RR intervals,” Cardiology Research and Practice, vol. 2009, Article ID 807379, 7 pages, 2009. View at Publisher · View at Google Scholar
  16. M. Costa, A. L. Goldberger, and C.-K. Peng, “Multiscale entropy analysis of complex physiologic time series,” Physical Review Letters, vol. 89, no. 6, Article ID 068102, 2002. View at Publisher · View at Google Scholar · View at Scopus
  17. S. Kuntamalla and R. G. R. Lekkala, “Reduced data dualscale entropy analysis of hrv signals for improved congestive heart failure detection,” Measurement Science Review, vol. 14, no. 5, pp. 294–301, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. L. Pecchia, P. Melillo, M. Sansone, and M. Bracale, “Discrimination power of short-term heart rate variability measures for CHF assessment,” IEEE Transactions on Information Technology in Biomedicine, vol. 15, no. 1, pp. 40–46, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. P. C. Ivanov, M. G. Rosenblum, C.-K. Peng et al., “Scaling behaviour of heartbeat intervals obtained by wavelet-based time-series analysis,” Nature, vol. 383, no. 6598, pp. 323–327, 1996. View at Publisher · View at Google Scholar · View at Scopus
  20. L. F. A. Campos, A. C. Silva, and A. K. Barros, “Independent component analysis and neural networks applied for classification of malignant, benign and normal tissue in digital mammography,” Methods of Information in Medicine, vol. 46, no. 2, pp. 212–215, 2007. View at Google Scholar · View at Scopus
  21. A. Ribeiro, A. Barros, E. Santana, and R. Diniz, “Tracking type 2 diabetes using sparse coding,” Diabetes, vol. 54, p. A409, 2015. View at Google Scholar
  22. A. J. Brockmeier and J. C. Príncipe, “Learning recurrent waveforms within EEGs,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 1, pp. 43–54, 2015. View at Publisher · View at Google Scholar
  23. R. D. Berger, J. P. Saul, and R. J. Cohen, “Assessment of autonomic response by broad-band respiration,” IEEE Transactions on Biomedical Engineering, vol. 36, no. 11, pp. 1061–1065, 1989. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Baselli, S. Cerutti, S. Civardi, A. Malliani, and M. Pagani, “Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms,” IEEE Transactions on Biomedical Engineering, vol. 35, no. 12, pp. 1033–1046, 1988. View at Publisher · View at Google Scholar · View at Scopus
  25. K. H. Chon, T. J. Mullen, and R. J. Cohen, “A dual-input nonlinear system analysis of autonomic modulation of heart rate,” IEEE Transactions on Biomedical Engineering, vol. 43, no. 5, pp. 530–544, 1996. View at Publisher · View at Google Scholar · View at Scopus
  26. R. Vetter, P. Celka, J. M. Vesin et al., “Subband modeling of the human cardiovascular system: new insights into cardiovascular regulation,” Annals of Biomedical Engineering, vol. 26, no. 2, pp. 293–307, 1998. View at Publisher · View at Google Scholar · View at Scopus
  27. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, “Heart rate variability: standards of measurement, physiological interpretation, and clinical use,” Circulation, vol. 93, no. 5, pp. 1043–1065, 1996. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Akay and E. Mulder, “Examining fetal heart-rate variability using matching pursuits,” IEEE Engineering in Medicine and Biology Magazine, vol. 15, no. 5, pp. 64–67, 1996. View at Publisher · View at Google Scholar · View at Scopus
  29. S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397–3415, 1993. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Mallat and Z. Zhang, “Adaptive time-frequency transform,” in Proceedings of the 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '93), vol. 3, pp. 241–244, IEEE, Minneapolis, Minn, USA, 1993. View at Publisher · View at Google Scholar
  31. F. Lucena, A. K. Barros, J. C. Príncipe, and N. Ohnishi, “Statistical coding and decoding of heartbeat intervals,” PLoS ONE, vol. 6, no. 6, Article ID e20227, 2011. View at Publisher · View at Google Scholar
  32. K. Umapathy, S. Krishnan, V. Parsa, and D. G. Jamieson, “Discrimination of pathological voices using a time-frequency approach,” IEEE Transactions on Biomedical Engineering, vol. 52, no. 3, pp. 421–430, 2005. View at Publisher · View at Google Scholar · View at Scopus
  33. M. P. Tarvainen, P. O. Ranta-aho, and P. A. Karjalainen, “An advanced detrending method with application to HRV analysis,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 2, pp. 172–175, 2002. View at Publisher · View at Google Scholar · View at Scopus
  34. The Criteria Committee of the New York Heart Association, Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels, Little Brown & Co, Boston, Mass, USA, 9th edition, 1994.
  35. S. Krishnan, R. M. Rangayyan, G. D. Bell, and C. B. Frank, “Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology,” IEEE Transactions on Biomedical Engineering, vol. 47, no. 6, pp. 773–783, 2000. View at Publisher · View at Google Scholar · View at Scopus
  36. B. Ghoraani and S. Krishnan, “A joint time-frequency and matrix decomposition feature extraction methodology for pathological voice classification,” EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 928974, 11 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. C. Berger, and R. J. Cohen, “Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control,” Science, vol. 213, no. 4504, pp. 220–222, 1981. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Hadase, A. Azuma, K. Zen et al., “Very low frequency power of heart rate variability is a powerful predictor of clinical prognosis in patients with congestive heart failure,” Circulation Journal, vol. 68, no. 4, pp. 343–347, 2004. View at Publisher · View at Google Scholar · View at Scopus
  39. F. Lucena, Y. Takeuchi, N. Ohnishi, A. K. Barros, and Y. Fujiwara, “Adaptive time-frequency interbeat,” in Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering (iCBBE '08), pp. 2056–2059, Shanghai, China, May 2008. View at Publisher · View at Google Scholar · View at Scopus
  40. F. Lucena, Y. Takeuchi, N. Ohnishi, A. K. Barros, and Y. Fujiwara, “Screening cardiac heart failure using biologically-inspired gabor-wavelets features,” in Brain Inspired Cognitive Systems, Springer, São Luís, Brazil, 2008. View at Google Scholar
  41. P. D. Welch, “The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms,” IEEE Transactions on Audio and Electroacoustics, vol. 15, no. 2, pp. 70–73, 1967. View at Publisher · View at Google Scholar · View at Scopus
  42. C. Thomas, Elements of Information Theory, Wiley-Interscience, New York, NY, USA, 2006.
  43. R. O. Duda, Pattern Classification, Wiley-Interscience, New York, NY, USA, 2nd edition, 2000.
  44. R. M. Rangayyan, Biomedical Signal Analysis: A Case Study Approach, IEEE Press, 2001.
  45. C. M. Bishop, Pattern Recognition and Machine Learning, Springer, Berlin, Germany, 2007.
  46. B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, 1996. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  47. R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, Morgan Kaufmann, Burlington, Mass, USA, 1995.
  48. F. Lucena, A. Cavalcante, A. K. Barros, Y. Takeuchi, and N. Ohnshi, “Wavelet entropy measure based on matching pursuit decomposition and its analysis to heartbeat intervals,” in Proceedings of the 17th International Conference on Neural Information Processing (ICONIP '10), Sydney, Australia, November 2010, vol. 6443, pp. 503–511, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  49. S. Blanco, A. Figliola, R. Q. Quiroga, O. A. Rosso, and E. Serrano, “Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function,” Physical Review E, vol. 57, no. 1, pp. 932–940, 1998. View at Google Scholar · View at Scopus
  50. S. A. Hunt, D. W. Baker, M. H. Chin et al., “ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary. A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee to revise the 1995 guidelines for the evaluation and management of heart failure),” Circulation, vol. 104, no. 24, pp. 2996–3007, 2001. View at Publisher · View at Google Scholar · View at Scopus
  51. Z.-G. Zhang, J.-L. Yang, S.-C. Chan, K. D.-K. Luk, and Y. Hu, “Time-frequency component analysis of somatosensory evoked potentials in rats,” BioMedical Engineering OnLine, vol. 8, article 4, 2009. View at Publisher · View at Google Scholar