Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2018, Article ID 1315357, 14 pages
https://doi.org/10.1155/2018/1315357
Research Article

Autodetection of J Wave Based on Random Forest with Synchrosqueezed Wavelet Transform

College of Information and Computer, Taiyuan University of Technology, Taiyuan, China

Correspondence should be addressed to Dengao Li; nc.ude.tuyt@oagnedil

Received 27 February 2018; Accepted 28 May 2018; Published 3 July 2018

Academic Editor: Yudong Cai

Copyright © 2018 Dengao Li 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. C. Antzelevitch and G.-X. Yan, “J wave syndromes,” Heart Rhythm, vol. 7, no. 4, pp. 549–558, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. M. J. Junttila, S. J. Sager, J. T. Tikkanen, O. Anttonen, H. V. Huikuri, and R. J. Myerburg, “Clinical significance of variants of J-points and J-waves: early repolarization patterns and risk,” European Heart Journal, vol. 33, no. 21, pp. 2639–2643, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. R. A. Shipley and W. R. Hallaran, “The four-lead electrocardiogram in two hundred normal men and women,” American Heart Journal, vol. 11, no. 3, pp. 325–345, 1936. View at Publisher · View at Google Scholar · View at Scopus
  4. W. Tomaszewski, Changement electrocardiographiques observes chez un homme mort de froid[J]. Arch Mal Coeur Vaiss, vol. 31, 525-528, 31, 1938.
  5. K. Nademanee, G. Veerakul, S. Nimmannit et al., “Arrhythmogenic marker for the sudden unexplained death syndrome in Thai men,” Circulation, vol. 96, no. 8, pp. 2595–2600, 1997. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Antzelevitch, P. Brugada, M. Borggrefe et al., “Brugada Syndrome: report of the second consensus conference,” Circulation, vol. 111, no. 5, pp. 659–670, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. C. Antzelevitch, “The Brugada Syndrome,” Journal of Cardiovascular Electrophysiology, vol. 9, no. 5, pp. 513–516, 1998. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Antzelevitch, “Brugada syndrome: historical perspectives and observations.,” European Heart Journal, vol. 23, no. 8, pp. 676–678, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. C. M. Otto, R. V. Tauxe, L. A. Cobb et al., “Ventricular fibrillation causes sudden death in Southeast Asian immigrants,” Annals of Internal Medicine, vol. 101, no. 1, pp. 45–47, 1984. View at Publisher · View at Google Scholar · View at Scopus
  10. P. Brugada and J. Brugada, “Right bundle branch block, persistent ST segment elevation and sudden cardiac death: a distinct clinical and electrocardiographic syndrome: a multicenter report,” Journal of the American College of Cardiology, vol. 20, no. 6, pp. 1391–1396, 1992. View at Publisher · View at Google Scholar · View at Scopus
  11. G.-X. Yan and C. Antzelevitch, “Cellular basis for the electrocardiographic J wave,” Circulation, vol. 93, no. 2, pp. 372–379, 1996. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Antzelevitch and B. Patocskai, “Ionic and cellular mechanisms underlying J wave syndromes,” J Wave Syndromes: Brugada and Early Repolarization Syndromes, pp. 33–76, 2016. View at Google Scholar · View at Scopus
  13. E. N. Clark, I. Katibi, and P. W. Macfarlane, “Automatic detection of end QRS notching or slurring,” Journal of Electrocardiology, vol. 47, no. 2, pp. 151–154, 2014. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Wang, H.-T. Wu, I. Daubechies, Y. Li, E. H. Estes, and E. Z. Soliman, “Automated J wave detection from digital 12-lead electrocardiogram,” Journal of Electrocardiology, vol. 48, no. 1, pp. 21–28, 2015. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Li, X. Liu, and J. Zhao, “An Approach for J Wave Auto-Detection Based on Support Vector Machine,” in Big Data Computing and Communications, vol. 9196 of Lecture Notes in Computer Science, pp. 453–461, Springer International Publishing, Cham, 2015. View at Publisher · View at Google Scholar
  16. D. Li, Y. Bai, and J. Zhao, “A Method for Automated J Wave Detection and Characterisation Based on Feature Extraction,” in Big Data Computing and Communications, vol. 9196 of Lecture Notes in Computer Science, pp. 421–433, Springer International Publishing, Cham, 2015. View at Publisher · View at Google Scholar
  17. L. Kijewski-Correa T, Full-scale measurements and system identification: A time-frequency perspective[D], University of Notre Dame, A time-frequency perspective[D]. University of Notre Dame, 2003.
  18. N. E. Huang, M. C. Wu, S. R. Long et al., “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” Proceedings A, vol. 459, no. 2037, pp. 2317–2345, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  19. I. Daubechies, J. Lu, and H.-T. Wu, “Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool,” Applied and Computational Harmonic Analysis , vol. 30, no. 2, pp. 243–261, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  20. R. J. Martis, U. R. Acharya, and L. C. Min, “ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform,” Biomedical Signal Processing and Control, vol. 8, no. 5, pp. 437–448, 2013. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm,” IEEE Transactions on Biomedical Engineering, vol. 32, no. 3, pp. 230–236, 1985. View at Google Scholar · View at Scopus
  22. F. Auger and P. Flandrin, “Improving the readability of time-frequency and time-scale representations by the reassignment method,” IEEE Transactions on Signal Processing, vol. 43, no. 5, pp. 1068–1089, 1995. View at Publisher · View at Google Scholar · View at Scopus
  23. E. Chassande-Mottin, I. Daubechies, F. Auger, and P. Flandrin, “Differential reassignment,” IEEE Signal Processing Letters, vol. 4, no. 10, pp. 293-294, 1997. View at Publisher · View at Google Scholar · View at Scopus
  24. Y.-C. Chen, M.-Y. Cheng, and H.-T. Wu, “Non-parametric and adaptive modelling of dynamic periodicity and trend with heteroscedastic and dependent errors,” Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 76, no. 3, pp. 651–682, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  25. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, Pa, USA, 1992. View at Publisher · View at Google Scholar · View at MathSciNet
  26. H. Yang, “Synchrosqueezed wave packet transforms and diffeomorphism based spectral analysis for 1D general mode decompositions,” Applied and Computational Harmonic Analysis , vol. 39, no. 1, pp. 33–66, 2015. View at Publisher · View at Google Scholar · View at MathSciNet
  27. C. Li and M. Liang, “A generalized synchrosqueezing transform for enhancing signal time-frequency representation,” Signal Processing, vol. 92, no. 9, pp. 2264–2274, 2012. View at Publisher · View at Google Scholar · View at Scopus
  28. S. Meignen, T. Oberlin, and S. McLaughlin, “A new algorithm for multicomponent signals analysis based on synchrosqueezing: with an application to signal sampling and denoising,” IEEE Transactions on Signal Processing, vol. 60, no. 11, pp. 5787–5798, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  29. G. Thakur, E. Brevdo, N. S. Fučkar, and H.-T. Wu, “The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications,” Signal Processing, vol. 93, no. 5, pp. 1079–1094, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. R. H. Herrera, J. Han, and M. van der Baan, “Applications of the synchrosqueezing transform in seismic time-frequency analysis,” Geophysics, vol. 79, no. 3, pp. V55–V64, 2013. View at Google Scholar · View at Scopus
  31. W. Williams J and M. Brown L, “Hero III A O. Uncertainty, information, and time-frequency distributions[C]//San Diego, '91,” in Proceedings of the International Society for Optics and Photonics, pp. 144–156, San Diego, CA, 1991.
  32. R. Sharma, R. B. Pachori, and U. R. Acharya, “Application of entropy measures on intrinsic mode functions for the automated identification of focal electroencephalogram signals,” Entropy, vol. 17, no. 2, pp. 669–691, 2015. View at Publisher · View at Google Scholar
  33. R. G. Baraniuk, P. Flandrin, A. J. Janssen, and O. J. Michel, “Measuring time-frequency information content using the Rényi entropies,” IEEE Transactions on Information Theory, vol. 47, no. 4, pp. 1391–1409, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  34. J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 278, no. 6, pp. H2039–H2049, 2000. View at Publisher · View at Google Scholar · View at Scopus
  35. N. V. Thakor and S. Tong, “Advances in quantitative electroencephalogram analysis methods,” Annual Review of Biomedical Engineering, vol. 6, pp. 453–495, 2004. View at Publisher · View at Google Scholar · View at Scopus
  36. S. M. Pincus, “Assessing serial irregularity and its implications for health,” Annals of the New York Academy of Sciences, vol. 954, pp. 245–267, 2001. View at Google Scholar · View at Scopus
  37. S.-N. Yu and M.-Y. Lee, “Wavelet-based multiscale sample entropy and chaotic features for congestive heart failure recognition using heart rate variability,” Journal of Medical and Biological Engineering, vol. 35, no. 3, pp. 338–347, 2015. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” Journal of Machine Learning Research, vol. 15, no. 1, pp. 3133–3181, 2014. View at Google Scholar · View at MathSciNet
  39. X.-Y. Pan, Y.-N. Zhang, and H.-B. Shen, “Large-scale prediction of human protein-protein interactions from amino acid sequence based on latent topic features,” Journal of Proteome Research, vol. 9, no. 10, pp. 4992–5001, 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  41. C. Ma, G. Luo, and K. Wang, “A combined random forests and active contour model approach for fully automatic segmentation of the left atrium in volumetric MRI,” BioMed Research International, vol. 2017, Article ID 8381094, 2017. View at Publisher · View at Google Scholar · View at Scopus
  42. E. Abdulhay, M. Alafeef, A. Abdelhay, and A. Al-Bashir, “Classification of Normal, Ictal and Inter-ictal EEG via Direct Quadrature and Random Forest Tree,” Journal of Medical and Biological Engineering, vol. 37, no. 6, pp. 843–857, 2017. View at Publisher · View at Google Scholar · View at Scopus
  43. S. Bernard, L. Heutte, and S. Adam, “Influence of hyperparameters on random forest accuracy,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface, vol. 5519, pp. 171–180, 2009. View at Google Scholar · View at Scopus
  44. L. N. Sharma, R. K. Tripathy, and S. Dandapat, “Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 7, pp. 1827–1837, 2015. View at Publisher · View at Google Scholar · View at Scopus
  45. B. L. Welch, “The generalization of student's problem when several different population variances are involved,” Biometrika, vol. 34, pp. 28–35, 1947. View at Google Scholar · View at MathSciNet · View at Scopus
  46. S.-N. Yu and Y.-H. Chen, “Electrocardiogram beat classification based on wavelet transformation and probabilistic neural network,” Pattern Recognition Letters, vol. 28, no. 10, pp. 1142–1150, 2007. View at Publisher · View at Google Scholar · View at Scopus
  47. X. Ma, J. Guo, and X. Sun, “Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection,” BioMed Research International, vol. 2015, Article ID 425810, 2015. View at Publisher · View at Google Scholar · View at Scopus
  48. S. Banerjee, “Identification of Elevated ST Segment and Deep Q Type MI Variant Using Cross Wavelet Transform and Hierarchical Classification From ECG Signals,” Journal of Medical and Biological Engineering, vol. 37, no. 4, pp. 492–507, 2017. View at Publisher · View at Google Scholar · View at Scopus
  49. C. Hsu W, C. Chang, and J. Lin C, A practical guide to support vector classification[J],.
  50. T. Jayasree, M. Bobby, and S. Muttan, “Sensor data classification for renal dysfunction patients using support vector machine,” Journal of Medical and Biological Engineering, vol. 35, no. 6, pp. 759–764, 2015. View at Publisher · View at Google Scholar · View at Scopus