Abstract
Heart rate variability (HRV) analysis can be successfully applied to automatic classification of cardiac rhythm abnormalities. This paper presents a novel Java-based computer framework for feature extraction from cardiac rhythms. The framework called HRVFrame implements more than 30 HRV linear time domain, frequency domain, time-frequency domain, and nonlinear features. Output of the framework in the form of .arff files enables easier medical knowledge discovery via platforms such as RapidMiner or Weka. The scope of the framework facilitates comparison of models for different cardiac disorders. Some of the features implemented in the framework can also be applied to other biomedical time-series. The thorough approach to feature extraction pursued in this work is also encouraged for other types of biomedical time-series.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Garcia, T.B., Holtz, N.E.: 12-Lead ECG: The Art of Interpretation. Jones and Bartlett Publishers, Sudbury (2001)
Malik, M., Bigger, J.T., Camm, A.J., Kleiger, R.E., Malliani, A., Moss, A.J., Schwartz, P.J.: Heart rate variability guidelines: Standards of measurement, physiological interpretation, and clinical use. Eur. Heart J. 17(3), 354–381 (1996)
Yang, A.C.-C., Hseu, S.-S., Yien, H.-W., Goldberger, A.L., Peng, C.-K.: Linguistic Analysis of the Human Heartbeat Using Frequency and Rank Order Statistics. Phys. Rev. Lett. 90(10), 108103 (2003)
Acharya, R.U., Joseph, K.P., Kannathal, N., Lim, C.M., Suri, J.S.: Heart rate variability: a review. Med. Bio. Eng. Comput. 44, 1031–1051 (2006)
Teich, M.C., Lowen, S.B., Jost, B.M., Vibe-Rheymer, K., Heneghan, C.: Heart-Rate Variability: Measures and Models. In: Akay, M. (ed.) Nonlinear Biomedical Signal Processing. Dynamic Analysis and Modeling, vol. II, ch. 6, pp. 159–213. IEEE Press, New York (2001)
de Carvalho, J.L.A., da Rocha, A.F., Nascimento, F.A.O., Neto, J.S., Junqueira, L.F.: Development of Matlab Software for Analysis of Heart Rate Variability. In: Proc. 6th Int. Conf. Sig. Proc. ICSP 2002, pp. 1488–1491. IEEE Press, Beijing (2002)
Perakakis, P., Joffily, M., Taylor, M., Guerra, P., Vila, J.: KARDIA: A Matlab software for the analysis of cardiac interbeat intervals. Comp. Meth. Prog. Biomed. 98, 83–89 (2010)
Niskanen, J.-P., Tarvainen, M.P., Ranta-aho, P.O., Karjalainen, P.A.: Software for advanced HRV analysis. Comp. Meth. Prog. Biomed. 76, 73–81 (2004)
Jovic, A., Bogunovic, N.: Random Forest-Based Classification of Heart Rate Variability Signals by Using Combinations of Linear and Nonlinear Features. In: Bamidis, P.D., Pallikarakis, N. (eds.) Proc. XII Mediterranean Conf. Medical and Biological Engineering and Computing MEDICON 2010, pp. 29–32. Springer, Berlin (2010)
Jovic, A., Bogunovic, N.: Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features. Artif. Intell. Med. (in press), doi:10.1016/j.artmed.2010.09.005
PhysioNet tool rdann, http://www.physionet.org/tutorials/physiobank-text.shtml#ann
Faust, O., Acharya, R.U., Krishnan, S.M., Min, L.C.: Analysis of cardiac signals using spatial filling index and time-frequency domain. BioMedical Engineering OnLine 3, 30 (2004)
Peng, C.-K., Havlin, S., Stanley, H.E., Goldberger, A.L.: Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos 5(1), 82–87 (1995)
Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of biological signals. Phys. Rev. E 71, 021906 (2005)
Costa, M., Goldberger, A.L., Peng, C.-K.: Broken asymmetry of the human heartbeat: Loss of time irreversibility in aging and disease. Phys. Rev. Lett. 95, 198102 (2005)
Witten, I.H., Frank, E.: Data mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco (2011)
RapidMiner, http://rapid-i.com/content/view/181/190/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jovic, A., Bogunovic, N. (2011). HRVFrame: Java-Based Framework for Feature Extraction from Cardiac Rhythm. In: Peleg, M., Lavrač, N., Combi, C. (eds) Artificial Intelligence in Medicine. AIME 2011. Lecture Notes in Computer Science(), vol 6747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22218-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-22218-4_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22217-7
Online ISBN: 978-3-642-22218-4
eBook Packages: Computer ScienceComputer Science (R0)