Classification of seizure and seizure free EEG signals using optimal mother wavelet and relative power
This paper presents an approach for the selection of mother wavelet for classification of EEG epilepsy signals .Wavelet transform is very popular for analyzing signals in time and frequency domain. But as there are various wavelet families exist and not a one fits to all, in this study author have e...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2020-10-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02879 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs21796_14201 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Salankar, Nilima |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a B. Nemade, Sangita |e author |
| 700 | 1 | 0 | |a P. Gaikwad, Varsha |e author |
| 245 | 0 | 0 | |a Classification of seizure and seizure free EEG signals using optimal mother wavelet and relative power |
| 260 | |b Institute of Advanced Engineering and Science, |c 2020-10-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21796 | ||
| 520 | |a This paper presents an approach for the selection of mother wavelet for classification of EEG epilepsy signals .Wavelet transform is very popular for analyzing signals in time and frequency domain. But as there are various wavelet families exist and not a one fits to all, in this study author have experimented with 51 wavelets from six different families haar (haar), daubechies (Db), symlet (Sym), coiflets (Coif), biorthogonal (Bior) and discrete meyer (Dmey). Optimal mother wavelet is selected on the basis of highest correlation between input signal and reconstructed signal. With Discrete wavelet transform four levels of decomposition have been used to create the five EEG rhythms delta, theta, alpha, beta and gamma. Five features kurtosis, skew, mean, standard deviation and relative power have been extracted from each decomposed level by using the optimal mother wavelet. Statistical significance of the extracted features has been computed by Mann Whitney U test with significance level p<0.05 and statistical parameters sensitivity, specificity and accuracy for performance evaluation of the classifier have been computed. Results shown that out of six experimented wavelet families, five families with eight wavelets have qualified the correlation test. Out of five extracted feature relative power is more statistically significant for all type of classification and all EEG bands .Classifier used is support vector machine and accuracy of classifier lies in the range of 74% to 100 % for 14 classifications between different subsets. | ||
| 540 | |a Copyright (c) 2020 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Discrete wavelet transform, EEG; Epilepsy; Mother wavelet; Relative power | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 20, No 1: October 2020; 197-205 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v20.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21796/14201 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/21796/14201 |z Get fulltext |