Abstract
The objective of this paper is to compare the performance of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) as the postclassifiers for classifications of the epilepsy risk levels obtained from extracted features through wavelet transforms and morphological filters from electroencephalogram (EEG) signals. The code converter acts as a level one classifier. The seven features such as energy, variance, positive and negative peaks, spike and sharp waves, events, average duration, and covariance are extracted from EEG signals. Out of which four parameters like positive and negative peaksand spike and sharp waves, events and average duration are extracted using Haar, dB2, dB4, and Sym 8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. Then, the performance of the code converter and classifiers are compared based on the parameters such as performance index (PI) and quality value (QV).The performance index and quality value of code converters are at low value of 33.26% and 12.74, respectively. The highest PI of 98.03% and QV of 23.82 are attained at dB2 wavelet with hard thresholding method for SVD classifier. All the postclassifiers are settled at PI value of more than 90% at QV of 20.
Highlights
The electroencephalogram (EEG) is a measure of cumulative firing of neurons in various parts of the brain [1]
This paper addresses the application and comparison of singular value decomposition (SVD), expectation maximization (EM), and modified expectation maximization (MEM) classifiers towards optimization of code converter outputs in the classification of epilepsy risk levels
Due to the nonlinearity obtained and the poor performance found in the code converters, an optimization was vital for the effective classification of the signals
Summary
The electroencephalogram (EEG) is a measure of cumulative firing of neurons in various parts of the brain [1] It contains information regarding changes in the electrical potential of the brain obtained from a given set of recording electrodes. Epileptic seizure is an abnormality in EEG gathering and is featured by short and episodic neuronal synchronous discharges with severely high amplitude This anomalous synchrony may happen in the brain locally (partial seizures) and is visible only in fewer channels of the EEG signal or including the entire brain, i.e., visible in all the channels of the EEG signal [12]
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