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Author(s): Sunandan Mandal, Kavita Thakur, Bikesh Kumar Singh, Heera Ram

Email(s): sunandan.mandal12@gmail.com

Address: School of Studies in Electronics & Photonics, PRSU Raipur, 492010, Chhattisgarh, India
Department of Biomedical Engineering, NIT Raipur, 492010, Chhattisgarh, India
Kalyan Post Graduate College, Bhilai Nagar, Durg, 491001, Chhattisgarh, India.

Published In:   Volume - 33,      Issue - 1,     Year - 2020

ABSTRACT:
54

Cite this article:
Mandal et al. (2020). Performance Evaluation of Spectrogram Based Epilepsy Detection Techniques Using Gray Scale Features. Journal of Ravishankar University (Part-B: Science), 33(1), pp. 01-07.


Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S. and Koh, J.E. (2015). Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowledge-Based Systems88: 85-96.

 

Amadasun, M. and King, R. (1989). Textural features corresponding to textural properties. IEEE Transactions on systems, man, and Cybernetics19(5): 1264-1274.

 

Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P. and Elger, C.E. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E64(6): 061907.

 

Chandani, M. and Kumar, A.  (2017). Classification of EEG Physiological Signal for the Detection of Epileptic Seizure by Using DWT Feature Extraction and Neural Network. International Journal of Neurologic Physical Therapy3(5): 38-43.

 

Chary, R.V.R., Lakshmi, D.D.R. and Sunitha, D.K.N. (2011). Image Retrieval and Similarity Measurement based on Image Feature. International Journal of Computer Science & Technology2(4): 385-389.

 

Christodoulou, C.I., Pattichis, C.S., Pantziaris, M., Tegos, T. and Nicolaides, A. (1998). Texture Analysis for the Classification of Carotid Plaques.

 

Chu, A., Sehgal, C.M. and Greenleaf, J.F. (1990). Use of gray value distribution of run lengths for texture analysis. Pattern Recognition Letters11(6): 415-419.

 

Costa, A.F., Humpire-Mamani, G. and Traina, A.J.M. (2012, August). An efficient algorithm for fractal analysis of textures. In 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images (pp. 39-46). IEEE.

 

Das, A.B. and Bhuiyan, M.I.H. (2016). Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. biomedical signal processing and control29: 11-21.

 

Dennis, J., Tran, H.D. and Chng, E.S. (2014). Analysis of spectrogram image methods for sound event classification. In Fifteenth Annual Conference of the International Speech Communication Association.

 

Dyer, C.R. and Rosenfeld A. (1976). Fourier texture features: Suppression of aperture effects. IEEE Transactions on Systems, Man, and Cybernetics6(10): 703-705.

 

EEG time series download page. Retrieved June 12, 2019, from http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3

 

Epilepsy. Retrieved July 29, 2019, from https://www.who.int/news-room/fact-sheets/detail/epilepsy

 

Galloway, M.M. (1975). Texture classification using gray level run length. Computer graphics and image processing4(2): 172-179.

 

Gonzalez, R.C. and Woods, R.E. (2010). Digital image processing using MATLAB. 2nd ed., Pearson Prentice Hall.

 

Haralick, R.M. and Shanmugam, K. (1973). Textural features for image classification. IEEE Transactions on systems, man, and cyberneticsSMC-3(6): 610-621.

 

Hu, M.K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory8(2): 179-187.

 

Khandpur, R. S. (2019). Handbook of Biomedical Instrumentation. Third ed., Chennai, Tamil Nadu: McGraw Hill Education (India) Private Limited.

 

Laws, K.I. (1980, December). Rapid texture identification. In Image processing for missile guidanceInternational Society for Optics and Photonics238: 376-381.

 

Mohammadpoory, Z., Nasrolahzadeh, M. and Haddadnia, J. (2017). Epileptic seizure detection in EEGs signals based on the weighted visibility graph entropy. Seizure50: 202-208.

 

Polikar, R. The wavelet tutorial. Retrieved August 05, 2019, from http://users.rowan.edu/~polikar/WTtutorial.html

 

Şengür, A., Guo, Y. and Akbulut, Y. (2016). Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain informatics3(2): 101-108.

 

Singh, B.K., Verma, K., Panigrahi, L. and Thoke, A.S. (2017). Integrating radiologist feedback with computer aided diagnostic systems for breast cancer risk prediction in ultrasonic images: An experimental investigation in machine learning paradigm. Expert Systems with Applications90: 209-223.

 

Siuly, S. and Li, Y. (2014). A novel statistical algorithm for multiclass EEG signal classification. Engineering Applications of Artificial Intelligence34: 154-167.

 

Weszka, J.S., Dyer, C.R. and Rosenfeld, A. (1976). A comparative study of texture measures for terrain classification. IEEE transactions on Systems, Man, and CyberneticsSMC-6(4): 269-285.

 

Wu, C.M. and Chen, Y.C. (1992). Statistical feature matrix for texture analysis. CVGIP: Graphical Models and Image Processing54(5): 407-419.

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