Detection of Epileptic Seizure Using Wavelet Analysis based Shannon Entropy, Logarithmic Energy Entropy and Support Vector Machine

Abstract

Epilepsy is a prevalent condition, mainly affecting the nervous system of the human body. Electroencephalogram (EEG) is used to evaluate and examine the seizures caused due to epilepsy. The issue of low precision and poor comprehensiveness is worked upon using dual tree- complex wavelet transform (DT-CWT), rather than discrete wavelet transform (DWT). Here, Logarithmic energy entropy (LogEn) and Shannon entropy (ShanEn) are taken as input features. These features are fed to Linear Support Vector Machine     (L-SVM) Classifier. For LogEn, accuracy of 100% for A-E, 99.34% for AB-E, and 98.67% for AC-E is achieved. While ShanEn combinations give accuracy of 96.67% for AB-E and 95.5% for ABC-E. These results showcase that our methodology is suitable for overcoming the problem and can become an alternate option for clinical diagnosis.