Abstract
This paper describes a novel intelligent analysis technique based upon bivariate Markov model that integrates morphological and temporal features with a rule-based interval analysis of ECG signals to localize and accurately classify the premature beats to four major classes: (1) Premature Atrial Complex (PAC), (2) Blocked PAC (B-PAC), (3) Premature Ventricular Complex (PVC), and (4) Premature Junctional Complex (PJC). The paper also describes a beat-pattern classification algorithm to sub classify premature beat-patterns into bigeminy, trigeminy and quadrigeminy. The approach utilizes two phases: (1) a training phase that builds bivariate Markov model from standardized databases of ECG signals, and (2) a dynamic phase that detects embedded P and R waves in T-waves of premature beats using a combination of area subtraction and clinically significant rule-based analysis of R-R intervals. It detects and classifies premature beats using graph matching based upon the forward-backward algorithm and performs a look ahead pattern analysis for the sub-classification of beat-patterns. The algorithms have been presented. The software has been implemented that uses a combination of MATLAB and C++ libraries. Performance results show that processing time is realistic for real-time detection with 98%–99% sensitivity for the premature beat classification and 95%-98% sensitivity for the beat pattern identification.
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
Similar content being viewed by others
References
Zipes, D.P., Camm, A.J., Borggrefe, M., Buxton, A.E., Chaitan, B., Fromer, M., et al.: ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. J. Am. Coll. Cardiol. 48(5), e247–e346 (2006)
Rautaharju, P.M., Surawicz, B., Gettes, L.S.: AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogram: part IV. J. Am. Coll. Cardiol. 53(11), 982–991 (2009)
Thong, T., McNames, J., Aboy, M., Goldstein, B.: Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans. Biomed. Eng. 51(4), 561–569 (2004)
Lerma, C., Glass, L.: Predicting the risk of sudden cardiac death. J. Physiol. 594(9), 2445–2458 (2016)
Chong, B.H., Pong, V., Lam, K.F., Liu, S., Zuo, M.L., Lau, Y.F., et al.: Frequent premature atrial complexes predict new occurrence of atrial fibrillation and adverse cardiovascular events. Europace 13(7), 942–947 (2011)
Tsipouras, M.G., Fotiadis, D.I., Sideris, D.: An arrhythmia classification system based on the RR-interval signal. Artif. Intell. Med. 33(3), 237–250 (2005)
Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdroff, J.M., Ivanov, P.C.H., Mark, R.G., et al.: Physiobank, physiotoolkit and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220
Palaniappan, R., Krishnan, S.: Detection of ectopic heartbeats using ECG and blood pressure. In: International Conference on Signal Processing & Communications (SPCOM 2004), pp. 573–576. Bangalore, India (2004)
Sayadi, O., Mohammad, B., Shamsollahi, M.B., Clifford, G.D.: Robust detection of premature ventricular contractions using a wave-based Bayesian framework. IEEE Trans. Biomed. Eng. 57(2), 353–362 (2010)
Lim, J.S.: Finding features for real-time premature ventricular contraction detection using a fuzzy neural network system. IEEE Trans. Neural Networks 20(3), 522–527 (2009)
Jankowski, S., Dusza, J.J., Wierzbowski, M. Oręziak, A.: SVM detection of premature ectopic excitations based on modified PCA. In: International Symposium of Biological and Medical Data Analysis, Lecture Notes in Computer Science, vol. 3745, pp. 173–183. Aveiro, Portugal, Nov 2005
Smilde, T.D.J., van Veldhuisen, D.J., van den Berg, M.P.: Prognostic value of heart rate variability and ventricular arrhythmias during 13-year follow-up in patients with mild to moderate heart failure. Clin. Res. Cardiol. 98(4), 233–239 (2009)
Garcia, T.B., Miller, G.T.: Arrhythmia Recognition: The Art of Interpretation. Jones and Bartlett (2013)
Kerin, N., Mori, I., Levy, M.N.: Ventricular quadrigeminy as a manifestation of concealed bigeminy. Circulation 52(6), 1023–1029 (1975)
Chiang, L.H., Kotanchek, M.E., Kordon, A.K.: Fault diagnosis based on Fisher discriminant analysis and support vector machines. Comput. Chem. Eng. 28(8), 1389–1401 (2004)
Gawde, P.R., Bansal, A.K., Nielson, J.A.: ECG analysis for automated diagnosis of subclasses of supraventricular arrhythmia. In: Arabnia, H.R., Deligiannidid, L. (eds.) International Conference on Health Informatics and Medical Systems, pp. 10–16. Las Vegas, Nevada, USA, July 2015
Elgendi M., Jonkman, M., De Boer, F.: Premature atrial complexes detection using the Fisher linear discriminant. In: 7th IEEE International Conference on Cognitive Informatics (ICCI 2008), pp. 83–88. Stanford University, CA, USA, Aug 2008
Ching, W., Ng, M.K., Fung, E.S.: Higher-order multivariate Markov chains and their applications. Linear Algebra Appl. 428(2–3), 492–507 (2008)
Tallarida, R.J., Murray, R.B.: Area under a curve: trapezoidal and Simpson’s rules. In: Manual of Pharmacologic Calculations, pp. 77–81. Springer, New York, NY (1987)
Mahmoodabadi, S.Z., Ahmadian, A., Abolhasani, M.D.: ECG feature extraction using Daubechies wavelets. In: Proceedings of the Fifth IASTED International Conference on Visualization, Imaging and Image Processing, pp. 343–348. Benidorm, Spain (2005)
Gawde, P.R., Bansal, A.K., Nielson, J.A.: Integrating Markov model and morphology analysis for finer classification of ventricular arrhythmia in real-time. In: IEEE International Conference on Biomedical & Health Informatics, pp. 409–412. Orlando, FL, USA (2017)
Russell, S., Norwig, P.: Artificial Intelligence—A Modern Approach, 3rd edn. Prentice Hall (2010)
Silva, I., Moody, G.: An open-source toolbox for analysing and processing PhysioNet databases in MATLAB and Octave. J. Open Res. Softw. 2(1), e27 (2014)
Everitt, B., Skrondal, A.: The Cambridge Dictionary of Statistics, vol. 106. Cambridge University Press, Cambridge, UK (2002)
Lin, C., Du, Y., Chen, Y., Chen, T.: Multiple ECG beats recognition in the frequency domain using grey relational analysis. In: Proceedings of the 28th IEEE EMBS Annual International Conference, pp. 2154–2158. New York City, USA. Sept 2006
Ideka, N., Takayanagi, K., Takeuchi, A., Takayanagi, A., Miyahara, H.: Two types of distribution patterns of bigeminy and trigeminy in long-term ECG: a model-based interpretation. In: Computers in Cardiology, vol. 35, pp. 1049–1052. Bologna, Italy, Sept 2008
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Gawde, P.R., Bansal, A.K., Nielson, J.A., Khan, J.I. (2019). Bivariate Markov Model Based Analysis of ECG for Accurate Identification and Classification of Premature Heartbeats and Irregular Beat-Patterns. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-01057-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01056-0
Online ISBN: 978-3-030-01057-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)