Epileptic Seizure Prediction Using Heart Rate Variability Signal Analysis

Document Type : Original Article

Authors

1 Department of Biomedical Engineering, K. N. Toosi University of Technology, Tehran, Iran

2 Neuroscience Group, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Abstract

Epilepsy is a neural disorder with unknown nature and epileptic patients suffer from the consequences of unexpected seizures. In this paper, we proposed a new method to predict epileptic seizures using heart rate variability (HRV) signal analysis. During preictal period of epilepsy, increasing in nervous activities of neurons affects the autonomic nervous system that disturbs heart rates. Therefore, epileptic seizures can be predicted through HRV monitoring. In our method, we extracted 12 features of HRV signal from different domains: time, frequency, time-frequency and non-linear domain. We used Multivariate Statistical Process Control (MSPC) algorithm for anomaly detection which is able to detect anomalies that cannot be detected by monitoring each variable independently. This algorithm has been applied to the clinical data collected from 17 patients. The obtained results demonstrated that the proposed method can predict seizure onset with an accuracy of 88.2%. The proposed HRV-based seizure prediction algorithm is more promising than the conventional EEG-based methods from the viewpoint of practical use.

Keywords

Main Subjects

References

[1] J. Christensen, M. Vestergaard, M. G. Pedersen, C. B. Pedersen, J. Olsen, and P. Sidenius, “Incidence and prevalence of epilepsy in Denmark,” Epilepsy Research, vol. 76, pp. 60–65, 2007.
[2] P. A. K. Wan, “Early identification of refractory epilepsy,” N Engl J Med, vol. 342, no. 5, pp. 314- 319, 1997.
[3] A. S. Zandi, R. Tafreshi, M. Javidan, and G. A. Dumont, “Predicting epileptic seizures in scalp EEG based on a variational bayesian gaussian mixture model of zero-crossing intervals,” IEEE Trans. Biomed. Eng., vol. 60, no. 5, pp. 1401–1413, 2013.
[4] L. Chisci, A. Mavino, G. Perferi, M. Sciandrone, C. Anile, and G. Colicchio, “Real-Time Epileptic Seizure Prediction Using AR Models and Support Vector Machines,” IEEE Trans. Biomed. Eng., vol. 57, no. 5, pp. 1124–1132, 2010.
[5] M. Z. Parvez, and M. Paul, “Seizure Prediction using Undulated Global and Local Features,” IEEE Trans. Biomed. Eng., vol. 64, no. 1, pp. 208–217, 2017.
[6] S. Sareen, S. K. Sood, and S. K. Gupta, “A Cloud-Based Seizure Alert System for Epileptic Patients That Uses Higher-Order Statistics,” Comput. Sci. Eng., vol. 18, no. 5, pp. 56–67, 2016.
[7] S. Wang, W. A. Chaovalitwongse, and S. Member, “Online Seizure Prediction Using an Adaptive Learning Approach,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 12, pp. 2854–2866, 2013.
[8] K.S.Eggleston et al., “Ictal tachycardia: The head-heart connection,”Seizure, vol. 23, pp. 496–505, 2014.
[9] C. Sevcencu and J. J. Struijk, “Autonomic alterations and cardiac changes in epilepsy,” Epilepsia, vol. 51, pp. 725–737, 2010.
[10] D. H. Kerem and A.B. Geva, “Forecasting epilepsy from the heart rate signal,” Med. Biol. Eng.Comput., vol. 43, pp. 230-239, 2005.
[11] K. Fujiwara et al., “Epileptic seizure monitoring by One-Class Support Vector Machine,” 2014 Asia-Pacific Signal Inf. Process. Assoc. Annu. Summit Conf. APSIPA 2014, pp. 5–8, 2014.
[12] S. Behbahani, N. J. Dabanloo, A. M. Nasrabadi, G. Attarodi, C. A. Teixeira, and A. Dourado,“Epileptic Seizure Behaviour from the Perspective of Heart Rate,” Computing in Cardiology, vol. 39,pp.117–120, 2012
[13] G. Valenza et al., “Predicting Seizures in Untreated Temporal Lobe Epilepsy using Point-Process Nonlinear Models of Heartbeat Dynamics,” IEEE, pp. 985–988, 2016.
[14] K. Fujiwara et al., “Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features,” IEEE Trans. Biomed. Eng., vol. 63, no. 6, pp. 1321–1332, 2016.
[15] K. Schiecke, M. Wacker, D. Piper, and F. Benninger, “Time-variant, Frequency-Selective, Linear and Non-linear Analyses of Heart Rate Variability in Children with Temporal Lobe Epilepsy,” Biomed. Eng. IEEE Trans. (Volume61, Issue 6), vol. 61, no. 6, pp. 1798–1808, 2014.
[16] A. J. Camm et al., “Guidelines heart rate variability—Standards of measurement, physiological interpretation, and clinical use,” Eur. Heart J., vol. 115, pp. 354–381, 1996.
[17] R. E. Kleiger et al., “Decreased heart rate variability and its associationwith increased mortality after acute myocardial infarction,” Amer. J.Cardiol., vol. 59, pp. 256–262, 1987.
[18] A. Malliani, “Cardiovascular neural regulation explored in the frequency domain,” Circulation, vol. 84, pp. 482–492, 1991.
[19] E. Abe et al., “Development of drowsiness detectionmethod by integratingheart rate variability analysis and multivariate statistical process control,”SICE J. Control. Meas. Syst. Integr., vol. 9, no. 1, pp. 001–008, Jan. 2016.
[20] J. Carvalho, A. Rocha, I. Santos, C. Itiki, L. Junqueira, and F. Nascimento, “A tool for time-frequency analysis of heart rate variability,” 23th IEEE EMBS Conference, pp. 2574-2577, 2003.
[21] Huikuri, H.V., Makikallio, T.H., Peng, C.K., Goldberger, A.L., Hintze, U. and Moller, M."Fractal correlation properties of R-R interval cynamics and mortality in patients with depressed left ventricular function after an acute myocardial infraction,"Circulation, vol. 101, no.1, pp.47-53, 2000.
[22] Yeh, R., Shieh, J., Han2, Y., Wang, Y., Tseng, S.,"Detrendet Fluctuation Analyses of shortterm heart rate variability in surgical intensive care units",Biomedical Engineering Applications , Basis & Communications, Vol.18, No.2 April 2006.

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