Genertation of Synthetic EEG Signal; Evaluation of Mutual Information and Correlation Coefficient

Saneesh Cleatus T, Thungamani M

Abstract


When it comes to the diagnosis and treatment of epilepsy, as well as the general quality of life of the patient, the electroencephalogram (EEG) is an often utilised as auxiliary test to aid in the process. It is the primary diagnostic test for epilepsy because it gives a continuous assessment of brain function with great temporal resolution over a long period of time, making it an excellent tool for early detection of epilepsy. Specifically, in this paper, we propose the creation of two Simulink models that can generate synthetic EEG data while maintaining the statistical characteristics of the EEG. In addition, we present the evaluation of two characteristics such as mutual information and correlation coefficient, in order to test the characteristics of any such synthetic generated data. The characteristics proposed here are tested with standard data available on online repository. Apart from using these characteristics for testing the validity of synthetic data, we may use these characteristics as features for machine learning applications.

Keywords


Electroencephalogram, Mutual Information, Correlation

Full Text:

PDF

References


S. Sanei and J. A. Chambers, EEG Signal Processing, vol. 1. 2013.

V. Svozilova, M. Mezl, V. Svozilova, and M. Mezl, “Modeling of the EEG signal Modeling of the EEG signal,” 2016.

Dennis Immanuel, T., and Chaitanya Srinivas LV. "A Simulink Model for Generating Epileptic Seizures and its Neurotherapy Treatment." International Journal of Bio Sciences and Engineering, Vol. 1, no. August, pp. 13–21, 2014.

Geng, S., Zhou, W., Yuan, Q. & Ma, Z. Bifurcation phenomenon of wendling’s EEG model. in International Symposium on Bioelectronics and Bioinformations 2011 111–114 (2011). doi:10.1109/ISBB.2011.6107658.

Tian, X., and Z. G. Xiao. "Simulation and control of epileptic EEG via a nonlinear model on Simulink." Proceedings. 2005 First International Conference on Neural Interface and Control, IEEE, 2005.

E. Dong, A. Jansen, and R. Model, “The Multi-frequency EEG Rhythms modeling based on two-parameter bifurcation of neural mass model,” no. 1, pp. 1564–1569, 2014.

Wendling, F., Hernandez, A., Bellanger, J.-J., Chauvel, P. & Bartolomei, F. Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG. J. Clin. Neurophysiol. 22, 343 (2005).

C. Bernard, M. Guye, P. Chauvel, and F. Bartolomei, “The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread,” Neuroimage, 2016.

S. Geng, W. Zhou, and Q. Yuan, “Bifurcation Phenomenon of Wendling’s EEG Model,” vol. 4, no. 1, pp. 3–6.

B. B. Kemp, A. K. H. Zwinderman, B. Tuk, H. A. C. Kamphuisen, and J. J. L. J. Oberyé, “Analysis of a Sleep-Dependent Neuronal Feedback Loop : The Slow-Wave Microcontinuity of the EEG,” vol. 47, no. 9, pp. 1185–1194, 2000.

M. Koppert, Computational modelling of epileptic seizure dynamics and control.

T. N. De Silva, X. Zhibo, Z. Rui, and M. Kezhi, “Causal Relation Identification Using Convolutional Neural Networks and Knowledge Based Features,” vol. 11, no. 6, pp. 696–701, 2017.

Srinivasa, S., 2005. A review on multivariate mutual information. Univ. of Notre Dame, Notre Dame, Indiana, 2(1).

Ali Shoeb. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. PhD Thesis, Massachusetts Institute of Technology, September 2009.

Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

Astolfi, L. et al. Estimation of the cortical connectivity by high-resolution EEG and structural equation modeling: Simulations and application to finger tapping data. IEEE Trans. Biomed. Eng. 52, 757–768 (2005).

Cejnar, P., Vysata, O., Valis, M. & Prochazka, A. The Complex Behaviour of a Simple Neural Oscillator Model in the Human Cortex. IEEE Trans. Neural Syst. Rehabil. Eng. 27, 337–347 (2019).




DOI: https://doi.org/10.26483/ijarcs.v12i5.6773

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Copyright (c) 2021 International Journal of Advanced Research in Computer Science