Effect of Wavelet Packet Log Energy Entropy on Electroencephalogram (EEG) Signals

Related Work On Entropy

Several works have been reported related to wavelet decomposition and entropy. Srinivasan et al. (2007) have used Approximate Entropy (ApEn), for detection of epilepsy using artificial neural networks. Data points of different frame sizes 173,256, 512, 1024 and 2048 were used to achieve an overall accuracy of 93.5% - 100% (Srinivasan et al. 2007). Yatindra Kumar et al. (2014) proposed a scheme for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. This method works based on discrete wavelet transforms (DWT) analysis and approximate entropy (ApEn) of EEG signals. EEG signals were decomposed by DWT to calculate approximation and detail coefficients, then ApEn values of the approximation and detail coefficients were calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG to detect seizures with 100% classification accuracy using artificial neural network (Yatindra Kumar et al. 2014). Guo et al. (2010) decompose original EEG signal first into several sub bands through four-level multi wavelet transform and then calculated ApEn feature to classify the EEGs using three-layer MLPNN with Bayesian regularization back-propagation training algorithms (Guo et al., 2010).