Nonlinear Analysis of EEG Dynamics in Different Epilepsy States Using Lagged PoincarÉ Maps

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Author(s)

Seyyed Abed Hosseini 1,*

1. Research Center of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2018.08.07

Received: 3 Apr. 2017 / Revised: 20 Apr. 2018 / Accepted: 14 Jun. 2018 / Published: 8 Aug. 2018

Index Terms

Electroencephalogram, Epilepsy, Lagged Poincaré map, Nonlinear analysis

Abstract

The Poincaré map and its width and length are known as a criterion for short-term variations of electroencephalogram (EEG) signals. This study evaluates the effect of time delay on changes in the width of the Poincaré map in the EEG signal during different epilepsy states. The Poincaré map is quantified by measuring the standard deviation over   (SD1) and the standard deviation over   (SD2). Poincaré maps are drawn with one to six delay in three sets, including normal, inter-ictal, and ictal. The results indicate that the width of the Poincaré map increases with increasing latency in the ictal state. During ictal state, the width of the Poincaré map is achieved by applying a unit delay of 102 ± 8.7 and a six-unit delay of 305 ± 13.6. The Poincaré map is shifted to lower values during ictal state. Also, the results indicate that with increasing delay in the ictal state, the increasing rate of SD1 value is higher than the previous ones, such as inter-ictal and normal. The Poincaré map of the EEG signal can discover the meaningful changes in the different epilepsy states. 

Cite This Paper

Seyyed Abed Hosseini, " Nonlinear Analysis of EEG Dynamics in Different Epilepsy States Using Lagged PoincarÉ Maps ", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.8, pp. 61-67, 2018. DOI: 10.5815/ijigsp.2018.08.07

Reference

[1]M. Amiri, E. Davoodi-Bojd, F. Bahrami, and M. Raza, “Bifurcation analysis of the Poincaré map function of intracranial EEG signals in temporal lobe epilepsy patients,” Mathematics and Computers in Simulation, vol. 81, no. 11, pp. 2471–2491, 2011.

[2]S. Dehuri, A. K. Jagadev, and S.-B. Cho, “Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble,” Procedia Computer Science, vol. 23, pp. 84–95, 2013.

[3]S. A. Hosseini, M. R. Akbarzadeh-T, and M. B. Naghibi-Sistani, “Qualitative and Quantitative Evaluation of EEG Signals in Epileptic Seizure Recognition,” International Journal of Intelligent Systems and Applications, vol. 6, pp. 41–46, 2013.

[4]S. A. Hosseini, “Epilepsy Recognition by Higher Order Spectra Analysis of EEG Signals,” In M. Khosrow-Pour (Ed.), Encyclopedia of Information Science and Technology, Third Edition, 2015. 

[5]R. B. Pachori and S. Patidar, “Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions,” Computer methods and programs in biomedicine, vol. 113, no. 2, pp. 494–502, 2014.

[6]S. A. Hosseini, M.-R. Akbarzadeh-T, and M.-B. Naghibi-Sistani, “Methodology for Epilepsy and Epileptic Seizure Recognition using Chaos Analysis of Brain Signals,” In K. Kolomvatsos, C. Anagnostopoulos, and S. Hadjiefthymiades (Eds.), Intelligent Technologies and Techniques for Pervasive Computing, p. 20, 2013.

[7]S. A. Hosseini, “Quantification of EEG signals for evaluation of emotional stress level,” MSc Thesis, Biomedical Department, Faculty of Engineering, Mashhad Branch, Islamic Azad University, 2009.

[8]S. A. Hosseini, “Epilepsy recognition using chaotic analysis of EEG signals based on qualitative and quantitative evaluation,” Advances in Cognitive Science, pp. 43–55, 2015.

[9]J. D. Bolaños et al., “Assessment of sedation-analgesia by means of poincaré analysis of the electroencephalogram,” in Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the, 2016, pp. 6425–6428.

[10]S. A. Hosseini and M. A. Khalilzadeh, “Emotional stress recognition system using EEG and psychophysiological signals: Using new labelling process of EEG signals in emotional stress state,” International Conference on in Biomedical Engineering and Computer Science (ICBECS), pp. 1–6, 2010.

[11]R. K. Goit, S. K. Jha, and B. N. Pant, “Alteration of cardiac autonomic function in patients with newly diagnosed epilepsy,” Physiological reports, vol. 4, no. 11, p. e12826, 2016.

[12]W. Ali, B. A. Bubolz, L. Nguyen, D. Castro, J. Coss-Bu, M. M. Quach, C. E. Kennedy, A. E. Anderson, and Y. C. Lai, “Epilepsy is associated with ventricular alterations following convulsive status epilepticus in children,” Epilepsia Open, vol. 2, no. 4, pp. 432-440, 2017.

[13]M. K. Moridani and H. Farhadi, “Heart rate variability as a biomarker for epilepsy seizure prediction,” Clinical Study, vol. 3, p. 8, 2017.

[14]R. S. Selvakumari and M. Mahalakshmi, “Epileptic seizure detection by analyzing high dimensional phase space via Poincaré section,” Multidimensional Systems and Signal Processing, pp. 1–11, 2018.

[15]B. Sharif and A. H. Jafari, “Design of an optimum Poincaré plane for extracting meaningful samples from EEG signals,” Australasian physical & engineering sciences in medicine, vol. 41, no. 1, pp. 13–20, 2018.

[16]B. Kamalizonouzi, “Optimal Inertial Sensor Placement and Motion Detection for Epileptic Seizure Patient Monitoring,” MSc thesis, The University of Western Ontario, 2012.

[17]M. Zabihi, S. Kiranyaz, A. B. Rad, A. K. Katsaggelos, M. Gabbouj, and T. Ince, “Analysis of high-dimensional phase space via Poincaré section for patient-specific seizure detection,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 3, pp. 386–398, 2016.

[18]B. Sharif and A. H. Jafari, “Prediction of epileptic seizures from EEG using analysis of ictal rules on Poincaré plane,” Computer Methods and Programs in Biomedicine, vol. 145, pp. 11–22, 2017.

[19]E. Ronkainen, H. Ansakorpi, H. V. Huikuri, V. V. Myllylä, J. I. T. Isojärvi, and J. T. Korpelainen, “Suppressed circadian heart rate dynamics in temporal lobe epilepsy,” Journal of Neurology, Neurosurgery & Psychiatry, vol. 76, no. 10, pp. 1382–1386, 2005.

[20]C. Kamath, “Analysis of EEG Dynamics in Epileptic Patients and Healthy Subjects Using Hilbert Transform Scatter Plots,” Open Access Library Journal, vol. 2, no. 1, p. 1, 2015.

[21]J. Jeppesen, S. Beniczky, P. Johansen, P. Sidenius, and A. Fuglsang-Frederiksen, “Detection of epileptic seizures with a modified heart rate variability algorithm based on Lorenz plot,” Seizure, vol. 24, pp. 1–7, 2015.

[22]E. Suorsa et al., “Heart rate dynamics in temporal lobe epilepsy—a long-term follow-up study,” Epilepsy Research, vol. 93, no. 1, pp. 80–83, 2011.

[23]R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David, and C. E. Elger, “Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state,” Physical Review E, vol. 64, no. 6, p. 061907, 2001.

[24]H. Poincaré, New methods of celestial mechanics, vol. 13. Springer Science & Business Media, 1992.

[25]C. K. Karmakar, A. H. Khandoker, J. Gubbi, and M. Palaniswami, “Complex Correlation Measure: a novel descriptor for Poincaré plot,” Biomedical engineering online, vol. 8, no. 1, p. 17, 2009.

[26]P. Castiglioni, C. Cerutti, M. di Rienzo, J. L. Elghozi, and N. Honzikova, “Glossary of terms used in time series analysis of cardiovascular data,” Working Group on Blood Pressure and Heart Rate Variability of the European Society of Hypertension, 2005.

[27]A. Brignol, T. Al-Ani, and X. Drouot, “Phase space and power spectral approaches for EEG-based automatic sleep–wake classification in humans: A comparative study using short and standard epoch lengths,” Computer methods and programs in biomedicine, vol. 109, no. 3, pp. 227–238, 2013.

[28]K. Hayashi, N. Mukai, and T. Sawa, “Poincaré analysis of the electroencephalogram during sevoflurane anesthesia,” Clinical Neurophysiology, vol. 126, no. 2, pp. 404–411, 2015.

[29]M. Rhaman, A. H. M. Karim, M. Hasan, and J. Sultana, “Successive RR Interval Analysis of PVC with Sinus Rhythm Using Fractal Dimension, Poincaré plot and Sample Entropy Method‖,” IJ Image, Graphics and Signal Processing, vol. 2, pp. 17–24, 2013.

[30]M. Fishman et al., “A method for analyzing temporal patterns of variability of a time series from Poincare plots,” Journal of Applied Physiology, vol. 113, no. 2, pp. 297–306, 2012.

[31]M. Brennan, M. Palaniswami, and P. Kamen, “Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?,” IEEE transactions on biomedical engineering, vol. 48, no. 11, pp. 1342–1347, 2001.