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.
Highlights
Epilepsy is a neurological disorder, involving repeated seizures, where involuntary movement of entire or partial body happens
Dual Tree Complex Wavelet Transform (DT-CWT) based feature extraction methods namely Shannon entropy (ShanEn) and Logarithmic energy entropy (LogEn) is showcased in this paper for epileptic seizure detection [3]
It was noted that LogEn gave higher accuracy than ShanEn
Summary
Epilepsy is a neurological disorder, involving repeated seizures, where involuntary movement of entire or partial body happens. Discrete Wavelet Transform (DWT), a type of time-frequency domain analysis, has been commonly used for algorithmic research and efficiency [2] This is used for epilepsy detection as a feature extractor which is required for finding accuracy corresponding to the data sets. Dual Tree Complex Wavelet Transform (DT-CWT) based feature extraction methods namely ShanEn and LogEn is showcased in this paper for epileptic seizure detection [3]. Thereafter, they are classified using Support Vector Machine (SVM). Dataset C, D and E signals are of epileptic patients Both Set C and D are pre-ictal and recorded during seizure free period from different part of the brain. EEG of normal person and epileptic person is shown in Figures 1a and 1b
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