nach oben

Information Systems Frontiers

Erschienen in:

01.09.2009

Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures

verfasst von: Heon Gyu Lee, Wuon-Shik Kim, Ki Yong Noh, Jin-Ho Shin, Unil Yun, Keun Ho Ryu

Erschienen in: Information Systems Frontiers | Ausgabe 4/2009

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

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.

Vorheriger Artikel Design of an RFID-based Healthcare Management System using an Information System Design Theory

Nächster Artikel Efficient mining of multilevel gene association rules from microarray and gene ontology

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

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

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.CrossRef

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.

Titel: Coronary artery disease prediction method using linear and nonlinear feature of heart rate variability in three recumbent postures
verfasst von: Heon Gyu Lee
Wuon-Shik Kim
Ki Yong Noh
Jin-Ho Shin
Unil Yun
Keun Ho Ryu
Publikationsdatum: 01.09.2009
Verlag: Springer US
Erschienen in: Information Systems Frontiers / Ausgabe 4/2009
Print ISSN: 1387-3326
Elektronische ISSN: 1572-9419
DOI: https://doi.org/10.1007/s10796-009-9155-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 4/2009

Editorial for the special issue of knowledge discovery and management in biomedical information systems

An automated bacterial colony counting and classification system

Efficient mining of multilevel gene association rules from microarray and gene ontology

Interactive survival analysis with the OCDM system: From development to application

Knowledge management in biomedical libraries: A semantic web approach

Design of an RFID-based Healthcare Management System using an Information System Design Theory