Brain Tumor Detection Using Electroencephalogram Linear and Non-Linear Features

Tabanfar, Zahra; Firouzabadi, Seyed Mohammad; Shankaei, Zeynab; Sharifi, Giv; Novin, Kambiz; Zoghi, Anahita

doi:10.22041/ijbme.2017.72077.1260

Document Type : Full Research Paper

Authors

¹ M.Sc. Graduated Student, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran Ph.D Student, Bioelectric Department, Biomedical Engineering Faculty, Amirkabir University of Technology, Tehran, Iran

² Professor, Medical Physics Department, School of Medical Science, Tarbiat Modares University, Tehran, Iran

³ Postdoctoral Researcher, Medical Physics Department, School of Medical Science, Tarbiat Modares University, Tehran, Iran

⁴ Department of Neurosurgery, Loghman e Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

⁵ Department of Radiation Oncology, Emam Hossein Medical center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

⁶ Department of Neurology, Loghman e Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

https://doi.org/10.22041/ijbme.2017.72077.1260

Abstract

In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F₃, F₄, T₃ and T₄ from 5 patients with brain tumor and 4 healthy participants were recorded. After preprocessing, linear features in time and frequency domains and nonlinear ones such as fractal dimensions and entropies were extracted. Afterwards, the differentiation between2 groups was analyzed using Davies-Bouldin Index, LDA, KNN and SVM classifiers. According to the results of Davies-Bouldin Index, RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed and RMS and Theta Absolute Power features in resting state with eyes opened, had the most distinction between the two groups. In this stage classification of two groups using single features was done and the most accuracy of 88.89% was obtained for RMS feature in resting state with eyes closed. At the end, classification of two groups using all selected features was conducted and the maximum accuracy of 82.54% was obtained for RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed. According to the results, EEG linear features have a good capability of detecting brain tumor. As these features are simple and have low computational complexity, they can be used in online applications especially for periodic screening tests.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

References

[1] Sharanreddy, M. and P. Kulkarni, Can EEG Test Helps in Identifying Brain Tumor. International Journal of Medical, Pharmaceutical Science and Engineering, 2013. 7(11): p. 230-235.

[2] Bateman, T.M., Advantages and disadvantages of PET and SPECT in a busy clinical practice. Journal of Nuclear Cardiology, 2012. 19(1): p. 3-11.

[3] Kanne, J.P. and T.A. Lalani, Role of computed tomography and magnetic resonance imaging for deep venous thrombosis and pulmonary embolism. Circulation, 2004. 109(12 suppl 1): p. I-15-I-21.

[4] Mindel, J., H.B. Newton, and J.L. Moore. The Neurophysiology of Central Nervous System Tumors. in Elsevier Inc. 2015.

[5] Martino, J., et al., Resting functional connectivity in patients with brain tumors in eloquent areas. Annals of neurology, 2011. 69(3): p. 521-532.

[6] Jochmann, T., et al., Influence of tissue conductivity changes on the EEG signal in the human brain–A simulation study. Zeitschrift für Medizinische Physik, 2011. 21(2): p. 102-112.

[7] Poulos, M., T. Felekis, and A. Evangelou, Is it possible to extract a fingerprint for early breast cancer via EEG analysis? Medical hypotheses, 2012. 78(6): p. 711-716.

[8] Poulos, M., et al., Endometrial Cancer Recognition via EEG Dependent upon 14-3-3 Protein Leading to an Ontological Diagnosis. World Academy of Science, Engineering and Technology, International Journal of Medical, Health, Biomedical, Bioengineering and Pharmaceutical Engineering, 2009. 3(7): p. 143-150.

[9] Decker, D.A. and J.R. Knott, The EEG in intrinsic supratentorial brain tumors: a comparative evaluation. Electroencephalography and clinical neurophysiology, 1972. 33(3): p. 303-310.

[10] Karameh, F.N. and M.A. Dahleh. Automated classification of EEG signals in brain tumor diagnostics. in American Control Conference, 2000. Proceedings of the 2000. 2000. IEEE.

[11] Jiang, Z., et al., Impaired fMRI activation in patients with primary brain tumors. Neuroimage, 2010. 52(2): p. 538-548.

[12] Boldyreva, G., et al., Analysis of fMRI-EEG data in patients with brain tumors during hand motor tasks. International Journal of Psychophysiology, 2010. 77(3): p. 321.

[13] B Sharanreddy, P.K.K., Detection of Primary Brain Tumor Present in EEG signal using Wavelet Transform and Neural Network. International Journal of Biological & Medical Research, 2013: p. 2855-2859.

[14] Zahra Tabanfar, S.M.F., Zeinab Shankayi, Guive Sharifi, Anahita Zoghi, Kambiz Novin, Does Brain Tumor Affect Brain Dynamics? , in 7th International Conference of Cognitive Science. 2017.

[15] Silipo, R., G. Deco, and H. Bartsch, Brain tumor classification based on EEG hidden dynamics. Intelligent Data Analysis, 1999. 3(4): p. 287-306.

[16] Dolisi, C., G. Suisse, and E. Delpont, Quantitative EEG abnormalities and asymmetries in patients with intracranial tumors. Electroencephalography and clinical Neurophysiology,1990. 76(1): P13-18

[17] Chetty, S. and G.K. Venayagamoorthy, An investigation into the Detection of brain tumours using electroencephalography (EEG) signals with Artificial neural networks. Computational Intelligence Group.

[18] Murugesan, M. and R. Sukanesh. Automated detection of brain tumor in EEG signals using artificial neural networks. in Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT'09. International Conference on. 2009. IEEE.

[19] Zahra Tabanfar, S.M.F., Zeynab Khodakarami, Zeinab Shankayi, Resting State EEG Analysis In Patients With Brain Tumor. Biotechnology Tarbiat Modares University (Biquarterly Publication), 2017: p. (In Press).

[20] Sharanreddy, M. and P. Kulkarni, Brain tumor epilepsy seizure identification using multi-wavelet transform, neural network and clinical diagnosis data. International Journal of Computer Applications, 2013. 67(2).

[21] Snaith, R.P., The hospital anxiety and depression scale. Health and quality of life outcomes, 2003. 1(1): p. 29.

[22] Urigüen, J.A. and B. Garcia-Zapirain, EEG artifact removal—state-of-the-art and guidelines. Journal of neural engineering, 2015. 12(3): p. 031001.

[23] Acharya, U.R., et al., Application of entropies for automated diagnosis of epilepsy using EEG signals: a review. Knowledge-Based Systems, 2015. 88: p. 85-96.

[24] Katebi, S. and M. Sabeti. Complexity measure as a feature to classify schizophrenic and healthy participants. in Computer Modelling and Simulation (UKSim), 2012 UKSim 14th International Conference on. 2012. IEEE.

[25] Naji, M., M. Firoozabadi, and S. Kahrizi. Evaluation of EMG features of trunk muscles during flexed postures. in Biomedical Engineering (ICBME), 2012 19th Iranian Conference of. 2012. IEEE.

[26] Chaovalitwongse, W.A., Y.-J. Fan, and R.C. Sachdeo, On the time series $ k $-nearest neighbor classification of abnormal brain activity. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2007. 37(6): p. 1005-1016.

[27] Bhardwaj, A., et al. Classification of human emotions from EEG signals using SVM and LDA Classifiers. in Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on. 2015. IEEE.

[28] Kotsiantis, S.B., I. Zaharakis, and P. Pintelas, Supervised machine learning: A review of classification techniques. 2007.

[29] Başar, E., et al., Gamma, alpha, delta, and theta oscillations govern cognitive processes. International journal of psychophysiology, 2001. 39(2): p. 241-248.

[30] Dinstein, I., D.J. Heeger, and M. Behrmann, Neural variability: friend or foe? Trends in cognitive sciences, 2015. 19(6): p. 322-328.

[31] Plante, D.T., et al., Altered slow wave activity in major depressive disorder with hypersomnia: a high density EEG pilot study. Psychiatry Research: Neuroimaging, 2012. 201(3): p. 240-244.

[32] Kumar, Y., M.L. Dewal, and R.S. Anand. Features extraction of EEG signals using approximate and sample entropy. in Electrical, Electronics and Computer Science (SCEECS), 2012 IEEE Students' Conference on. 2012.

Iranian Journal of Biomedical Engineering

Brain Tumor Detection Using Electroencephalogram Linear and Non-Linear Features

References

References

Volume 10, Issue 3
October 2016
Pages 211-221

Brain Tumor Detection Using Electroencephalogram Linear and Non-Linear Features

References

References

Volume 10, Issue 3October 2016Pages 211-221

Volume 10, Issue 3
October 2016
Pages 211-221