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Articles

Fourier Transform and Autoregressive HRV Features in Prediction and Classification of Breast Cancer

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  • J. P. Niskanen, M. P. Tarvainen, P. O. Ranta-Aho, and P. A. Karjalainen, “Software for advanced HRV analysis,” Comput. Methods Programs Biomed., Vol. 76, no. 1, pp. 73–81, October 2004.
  • R. Acharya, N. Kannathal, and S. M. Krishnan, “Comprehensive analysis of cardiac health using heart rate signals,” Physiol. Meas., Vol. 25, no. 5, pp. 1139, August 2004.
  • M. P. Tarvainen, J. P. Niskanen, J. A. Lipponen, P. O. Ranta- aho, and P. A. Karjalainen, “Kubios HRV–heart rate variability analysis software,” Comput. Meth. Prog. Biomed., Vol. 113, no. 1, pp. 210–220, January 2014.
  • S. Akselrod, D. Gordon, F. A. Ubel, D. C. Shannon, A. C. Barger, and R. J. Cohen, “Power spectrum analysis of heart rate fluctuations: a quantitative probe of beat-to-beat cardiovascular control,” Science, Vol. 213, pp. 220–222, July 1981.
  • Y. Aggarwal, N. Singh, S. Ghosh, and R. K. Sinha, “Eye gaze-induced mental stress alters the heart rate variability analysis,” J. Clin. Eng., Vol. 39, no. 2, pp. 79–89, April 2014.
  • H. Dabiré, D. Mestivier, J. Jarnet, M. E. Safar, and N. P. Chau, “Quantification of sympathetic and parasympathetic tones by nonlinear indexes in normotensive rats,” Amer. J. Physiol. Heart Circul. Physiol., Vol. 275, no. 4, pp. H1290–H1297, October 1998.
  • F. Paulin, and A. Santhakumaran, “Classification of breast cancer by comparing back propagation training algorithms,” Int. J. Computer Sci. Eng., Vol. 3, no. 1, pp. 327–332, January 2011.
  • E. C. Fear, P. M. Meaney, and M. A. Stuchly, “Microwaves for breast cancer detection,” IEEE Potentials, Vol. 22, no. 1, pp. 12–18, February 2003.
  • M.-W. Huang, C.-W. Chen, W.-C. Lin, S.-W. Ke, and C.-F. Tsai, “SVM and SVM ensembles in breast cancer prediction,” PLoS One, Vol. 12, no. 1, pp. e0161501, January 2017.
  • J. Cong, B. Wei, Y. He, Y. Yin, and Y. Zheng, “A selective ensemble classification method combining mammography images with ultrasound images for breast cancer diagnosis,” Comput. Math. Methods. Med. Vol. 2017, 2017. Article no. 4896386.
  • C.-J. Tseng, C.-J. Lu, C.-C. Chang, G.-D. Chen, and C. Cheewakriangkrai, “Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence,” Artif. Intell. Med. Vol. 78, pp. 47–54, June 2017.
  • T. Kiyan, and T. Yildirim, “Breast cancer diagnosis using statistical neural networks,” J. Electr. Electron. Eng., Vol. 4, no. 2, pp. 1149–1153, 2004.
  • Y. Rejani, and S. T. Selvi. “Early detection of breast cancer using SVM classifier technique”, arXiv preprint arXiv:0912.2314, Dec 2009.
  • R. S. Shukla, and Y. Aggarwal, “Spectral analysis to evaluate the effect of treatment on autonomic nervous system in pulmonary metastasis,” Int. J. Eng. Technol. Sci. Res., Vol. 4, no. 6, pp. 501–506, June 2017.
  • R. S. Shukla, and Y. Aggarwal, “Heart rate variability time-domain analysis in pulmonary metastasis to assess performance status,” Indian J. Sci. Res., Vol. 14, no. 2, pp. 540–545, 2017.
  • C.-C. Chang, and C.-J. Lin. “LIBSVM a library for support vector machines,” 2016 http://www.csie.ntu.edu.tw/~cjlin/libsvm/index.html.
  • G. Mercier, and M. Lennon. “Support vector machines for hyperspectral image classification with spectral-based kernels”, InGeoscience and Remote Sensing Symposium, 2003. IGARSS'03. Proceedings. 2003 IEEE International 2003 Jul 21 (Vol. 1, pp. 288–290). IEEE.
  • N. L. Pochet, and J. A. Suykens, “Support vector machines versus logistic regression: improving prospective performance in clinical decision-making,” Ultrasound Obstet. Gynecol., Vol. 27, no. 6, pp. 607–608, June 2006.
  • H. Bettermann, M. Kröz, M. Girke, and C. Heckmann, “Heart rate dynamics and cardiorespiratory coordination in diabetic and breast cancer patients,” Clin. Physiol. Funct. Imaging, Vol. 21, no. 4, pp. 411–420, July 2001.
  • L. Claudia, I. Oscar, P. G. Hector, and V. J. Marco, “Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients,” Clin. Physiol. Func. Im., Vol. 23, no. 2, pp. 72–80, March 2003.
  • T. Åkerstedt, and S. Folkard, “Validation of the S and C components of the three-process model of alertness regulation,” Sleep, Vol. 18, no. 1, pp. 1–6, January 1995.
  • T. Tran, N. Wijesuriya, M. Tarvainen, P. Karjalainen, and A. Craig, “The relationship between spectral changes in heart rate variability and fatigue,” J. Psychophysiol., Vol. 23, no. 3, pp. 143–151, January 2009.
  • V. B. Wyller, R. Barbieri, E. Thaulow, and J. P. Saul, “Enhanced vagal withdrawal during mild orthostatic stress in adolescents with chronic fatigue,” Ann. Noninvasive Electrocardiol., Vol. 13, no. 1, pp. 67–73, January 2008.
  • J.-K. Chiang, M. Koo, T. B. J. Kuo, and C.-H. Fu, “Association between cardiovascular autonomic functions and time to death in patients with terminal hepatocellular carcinoma,” J. Pain Symptom Manage., Vol. 39, pp. 673–679, 2010.
  • J. Giese-Davis, F. H. Wilhelm, R. Tamagawa, O. Palesh, E. Neri, C. B. Taylor, H. C. Kraemer, and D. Spiegel, “Higher vagal activity as related to survival in patients with advanced breast cancer: an analysis of autonomic dysregulation,” Psychosom. Med., Vol. 77, no. 4, pp. 346–355, May 2015.
  • J. Hayano, Y. Sakakibara, A. Yamada, N. Ohte, T. Fujinami, K. Yokohama, Y. Watanabe, and K. Takata, “Decreased magnitude of heart rate spectral components in coronary artery disease. Its relation to angiographic severity,” Circulation, Vol. 81, no. 4, pp. 1217–1224, April 1990.
  • T. Komatsu, T. Kimura, K. Nishiwaki, Y. Fujiwara, K. Sawada, and Y. Shimada, “Recovery of heart rate variability profile in patients after coronary artery surgery,” Anesth. Analg., Vol. 85, no. 4, pp. 713–718, October 1997.
  • E. M. Ekholm, E. K. Salminen, H. V. Huikuri, J. Jalonen, K. J. Antila, T. A. Salmi, and V. T. Rantanen, “Impairment of heart rate variability during paclitaxel therapy,” Cancer, Vol. 88, no. 9, pp. 2149–2153, May 2013.
  • Y. V. Chesnokov, “Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods,” Artif. Intell. Med., Vol. 43, no. 2, pp. 151–165, 2008.
  • Y. Aggarwal, B. M. Karan, B. N. Das, T. Aggarwal, and R. K. Sinha, “Backpropagation ANN-based prediction of exertional heat illness,” J. Med. Syst., Vol. 31, no. 6, pp. 547–550, December 2007.
  • U. R. Acharya, O. Faust, S. V. Sree, D. N. Ghista, S. Dua, P. Joseph P, V. T. Ahamed, N. Janarthanan, and T. Tamura, “An integrated diabetic index using heart rate variability signal features for diagnosis of diabetes,” Comput. Methods Biomech. Biomed. Engin., Vol. 16, no. 2, pp. 222–234, February 2013.
  • R. S. Shukla, and Y. Aggarwal. “Time-domain heart rate variability-based computer-aided prognosis of lung cancer”, Indian J. Cancer, doi:10.4103/ijc.IJC_395_17
  • H. Hong, L. Jiuyong, A. Plank, H. Wang, and G. Daggard, “A comparative study of classification methods for microarray data analysis,” in Proceedings of the fifth Australasian conference on data mining and analytics. Australian Computer Society, Inc., vol 61, November 2006, pp. 33–37.

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