Abstract
In present study, we proposed not only a novel methodology useful in developing the various features of heart rate variability (HRV), but also a suitable prediction model to enhance the reliability of medical examinations and treatments for coronary artery disease. In order to develop the various features of HRV, we analyzed HRV for three recumbent postures. The interaction effects between the recumbent postures and groups of normal people and heart patients were observed based on linear and nonlinear features of HRV. Forty-three control subjects and 64 patients with coronary artery disease participated in this study. In order to extract various features, we tested five classification methods and evaluated performance of classifiers. As a result, SVM and CMAR (gave about 72–88% goodness of accuracy) outperformed the other classifiers.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
The following terminology is used when referring to the counts tabulated in a confusion matrix and the counts can also be expressed in terms of percentages.
TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative
References
Agrawal, R., & Srikant, R. (1994). Fast algorithms mining association rules in large databases. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB’94), Santiago, Chile, pp. 487–499.
Bhanot, G., Alexe, G., Venkataraghavan, B., & Levine, A. (2006). A robust meta-classification strategy for cancer detection from MS data. Proteomics, 6(2), 592–604.
Brennan, M., Palaniswami, M., & Kamen, P. (2001). Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Transaction Biomedical Engineering, 48(11), 1342–1347.
Calcagnini, G., Censi, F., Cesarini, A., Lino, S., & Cerutti, S. (1998). Self-similar properties of long term heart rate variability assessed by discrete wavelet transform. Computers in Cardiology, 25, 333–336.
Chen, J., & Greiner, R. (1999). Comparing Bayesian network classifiers. In Proceedings of 15th Conference on Uncertainty in Artificial Intelligence, pp. 101–108.
Coenen, F. (2004). The LUCS-KDD Group, Department of computer science. The University of Liverpool, UK (Online), Available: http://www.cSc.liv.ac.uk/∼frans/KDD/.
Fayyad, U., & Irani, K. (1993). Multi-Interval discretization of continuous-valued attributes for classification learning. In Proceedings of 13th International Joint Conference on Artificial Intelligence, pp. 1022–1027.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. The Journal of Machine Learning Research, 3, 1157–1182.
Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In Proceeding of the ACM-SIGMOD International Conference Management of Data (SIGMOD'00), Dallas, USA, pp. 1–12.
Kannathal, N., Acharya, U. R., Lim, C. M., & Sadasivan, P. K. (2005). Characterization of EEG-A comparative study. Computer Methods and Programs in Biomedicine, 80(1), 17–23.
Kleiger, R. E., Miller, J. P., Bigger, J. T., & Moss, A. J. (1987). Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. American Journal of Cardiology, 59(4), 256–262.
Kim, W. S., Yoon, Y. Z., Bae, J. H., & Soh, K. S. (2005). Nonlinear characteristics of heart rate time series: influence of three recumbent positions in patients with mild or severe coronary artery disease. Physiological Measurement, 26, 517–529.
Lattin, J., Carroll, J. D., & Green, P. E. (2003). Analyzing multivariate data. Canada: Thomson Learning.
Lee, H. G., Noh, K. Y., & Ryu, K. H. (2006). Cardiovascular disease diagnosis method by emerging patterns. Lecture note in artificial intelligence. Berlin: Springer, 819–826, 4093/2006.
Li, W., Han, J., & Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In Proceedings of 2001 IEEE International Conference on Data Mining, San Jose, CA, pp. 369–376.
Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. In Proceedings of the 4th International Conference Knowledge Discovery and Data Mining (KDD'98), New York, USA, pp. 80–86.
Lombardi, F., Malliani, A., Pagani, M., & Cerutti, S. (1996). Heart rate variability and its sympatho-vagal modulation. Cardiovascular Research, 32(2), 208–216.
Miyamoto, S., Fujita, M., Sekiguchi, H., Sekiguchi, H., Okano, Y., & Nagaya, N. (2001). Effects of posture on cardiac autonomic nervous activity in patients with congestive heart failure. Journal of the American College of Cardiology, 37(7), 1788–1793.
Miyamoto, S., Fujita, M., Tambara, K., Sekiguchi, H., Eiho, S., & Hasegawa, K. (2004). Circadian variation of cardiac autonomic nervous activity is well preserved in patients with mild to moderate chronic heart failure: effect of patient position. International Journal of Cardiology isPpublished by Elsevier, 93(2), 247–252.
Peng, C. K., Havlin, S., Stanley, H. E., & Goldberger, A. L. (1995). Quantification of scaling exponents and crossover phenomena in nonstationary heart-rate time series. Chaos, 5, 82–87.
Peng, C. K., Hausdorff, J. M., & Goldberger, A. L. (2000). Fractal mechanisms in neural control: Human heartbeat and gait dynamics in health and disease. Self-organized biological dynamics and nonlinear control pp. 66–96. Cambridge: Cambridge University Press.
Pincus, S. M., & Goldberger, A. L. (1994). Physiological time-series analysis: What does regularity quantify? American Journal of Physiological, 266(H1), 643–656.
Pumprla, J., Howorka, K., Groves, D., Chester, M., & Nolan, J. (2002). Functional assessment of heart rate variability: Physiological basis and practical applications. International Journal of Cardiology, 84(1), 1–14.
Quinlan, J. (1993). C4.5: Programs for machine learning. San Mateo: Morgan Kaufmann.
Signorini, M. G., Guzzetti, S., Manzoni, C., Milani, S., & Cerutti, S. (1998). Multiparametric analysis of HRV signal by linear and nonlinear methods in heart failure patient population. In Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20, 322–325.
Takens, F. (1981). Detecting strange attractors in turbulence. Lecture notes in mathematics pp. 366–381. Berlin: Springer 898/1981.
Tompkins, W. J. (1995). Bimedical digital signal processing. Upper Saddle River: Prentice Hall 07458.
Witten, I. H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques. San Mateo: Morgan Kaufmann.
Acknowledgements
This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. R01-2007-000-10926-0), by the Korea Research Foundation Grant funded by the Korea Government (MEST)/Chungbuk BIT Research-Oriented University Consortium, and by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MEST) (Grant No. R11-2008-014-02002-0).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, H.G., Kim, WS., Noh, K.Y. et al. Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures. Inf Syst Front 11, 419–431 (2009). https://doi.org/10.1007/s10796-009-9155-2
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10796-009-9155-2