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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

DOI: 10.52228/JRUB.2020-33-1-1  

Electroencephalogram (EEG) is most common instrument for treatment and diagnosis of brain related diseases. Analysis of EEG signals for treatment of patient is time consuming and not easy task for neurologist. There is always a chance of human error. The purpose of this paper is to present an automatic detection model for epileptic seizure from EEG signals. To fulfill this objective, EEG signals are preprocessed and converted into spectrogram images using Short Time Fourier Transform (STFT). From this spectrogram images gray scale features are extracted. Support Vector Machine (SVM) with six different kernel functions and three data division protocols are utilized for performance evaluation of proposed model. Results show that quadratic SVM classifier has achieved highest classification accuracy.

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.DOI: https://doi.org/10.52228/JRUB.2020-33-1-1

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