Abstract
Recurrence is a basic property of scattering dynamical systems. Recurrence plots visualize the recurrent behavior of these systems. The cardiovascular system in sudden cardiac death (SCD) patients appeared to be such dynamical system as the patient meets sudden death within few minutes or an hour after onset of the symptoms. Therefore, the present study was designed to explore an algorithm to predict the SCD 1 h before the “SCD Onset” using recurrence plots of the RR-intervals. The MIT-BIH database was used for designing the samples of the study. The 5-min ECG signals of the SCD patients which were 1 h before the SCD onset and just 5 min before the SCD onset were preprocessed for noise removal and RR-interval extraction. The RR-intervals were then processed to obtain a set of nonlinear features using recurrence plots. These features were checked for correlation among the SCD patients using Spearman’s function of correlation. The classification of the optimal features into normal control subjects and SCD was implemented using classifier k-Nearest Neighbor or k-NN classifier. A maximum accuracy of 98.44% was obtained using k-NN. The results indicate that, the proposed algorithm can efficiently identify the person at risk of progressive SCD 1 h before, which would be helpful in supporting sufficient time for the medical staff attending the patient to respond with preventive treatment.
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
References
E. Ladich, R. Virmani, A. Burke, Sudden cardiac death not related to coronary atherosclerosis. Toxicol. Pathol. 34, 52 (2006)
S.S. Chug, Early identification of risk factors for sudden cardiac death. Nat. Rev. Cardiol. 7, 318–326 (2010)
S. Patil et al., Intelligent and effective heart attack prediction system using data mining and artificial neural network. Eur. J. Sci. Res. 31(4), 642–656 (2009)
T. Jilani et al., Acute coronary syndrome prediction using data mining techniques-an application. Int. J. Inf. Math. Sci. 5(4), 295–299 (2009)
M.E. Lammert et al., Electrocardiographic predictors of out-of hospital sudden cardiac arrest in patients with coronary artery disease. Am. J. Cardiol. 109(9), 1278–1282 (2012)
E. Ebrahimzadeh, M. Pooyan, Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals. J. Biomed. Sci. Eng. 4, 699–706 (2011)
H. Fujita, U.R. Acharya, Vidya K. Sudarshan, D.N. Ghista, S.V. Sree, W.J.E. Lim, J.E.W. Koh, Sudden cardiac death (SCD) prediction based on non-linear heart rate variability features and SCD index. Appl. Soft Comput. 43, 510–519 (2016)
L. Murukesan, M. Murugappan, I. Omar, S. Khatun, S. Murugappan, Time domain features based sudden cardiac arrest prediction using machine learning algorithms. J. Med. Imaging Health Inf. 5, 1267–1271 (2015)
L. Murukesan, M. Murugappan, M. Iqbal, M. Saravanan, Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features. J. Med. Imaging Health Inf. 4, 1–12 (2014)
F. Taken, in Detecting Strange Attractors in Turbulence Dynamical Systems and Turbulence. Lecture Notes in Mathematics, vol. 898 (Springer, Berlin, 1981), pp. 366–381
A.L. Goldberger, L.A.N. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 101, e215–e220 (2000)
J. Pan, W. Tompkins, A real time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32, 230–236 (1985)
H. Yang, Multiscale recurrence quantification analysis of spatial vectorcardiogram (VCG) signals. IEEE Trans. Biomed. Eng. 58(2), 339–347 (2011)
Y. Chen, H. Yang, Multiscale recurrence analysis of long-term nonlinear and nonstationary time series. Chaos Solitons Fractals 45(7), 978–987 (2012)
M.B. Kennel, R. Brown, H.D. Abarbanel, Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403–3411 (1992)
A.M. Fraser, H.L. Swinney, Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134–1140 (1986)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Devi, R., Tyagi, H.K., Kumar, D. (2019). Recurrence Plot Features of RR-Interval Signal for Early Stage Mortality Identification in Sudden Cardiac Death Patients. In: Krishna, C., Dutta, M., Kumar, R. (eds) Proceedings of 2nd International Conference on Communication, Computing and Networking. Lecture Notes in Networks and Systems, vol 46. Springer, Singapore. https://doi.org/10.1007/978-981-13-1217-5_40
Download citation
DOI: https://doi.org/10.1007/978-981-13-1217-5_40
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1216-8
Online ISBN: 978-981-13-1217-5
eBook Packages: EngineeringEngineering (R0)