Affiliation:
1. ECE, Bannari Amman Institute of Technology, Sathyamangalam 638401, India
2. IT, Bannari Amman Institute of Technology, Sathyamangalam 638401, India
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 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 peaks, spike and sharp waves, events, and average duration are extracted using Haar, dB2, dB4, and Sym8 wavelet transforms with hard and soft thresholding methods. The above said four features are also extracted through morphological filters. 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.
Subject
General Engineering,General Mathematics
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献