Abstract
Epilepsy is a chronic electrophysiological disorder characterized by repetitive seizures. Electroencephalogram (EEG) signals represent the dynamic condition of the brain. Variations in the EEG signals cannot be predicted by ocular examination. Thus, signal processing methods are used to extract the hidden information from the EEG signals. In this work, three different types of EEG signals (normal, epileptic, background) are used and features are extracted using wavelet entropies and energies. ANOVA is performed followed by classification. A graphical user interface (GUI) is introduced that can help in the automatic classification of the EEG signals. Various classifiers such as decision tree, k-nearest neighbour, support vector machine and fuzzy classifiers are used. Classification accuracy of 97% is achieved using decision tree classifier with GUI.
| Original language | English |
|---|---|
| Pages (from-to) | 868-873 |
| Number of pages | 6 |
| Journal | Journal of Medical Imaging and Health Informatics |
| Volume | 4 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 01-01-2014 |
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All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
- Health Informatics
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Automated classification of epileptic electroencephalogram signals using wavelet entropies and energies. / Bairy, G. Muralidhar; Bhat, Shreya; Niranjan, U. C.
In: Journal of Medical Imaging and Health Informatics, Vol. 4, No. 6, 01.01.2014, p. 868-873.Research output: Contribution to journal › Article
TY - JOUR
T1 - Automated classification of epileptic electroencephalogram signals using wavelet entropies and energies
AU - Bairy, G. Muralidhar
AU - Bhat, Shreya
AU - Niranjan, U. C.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Epilepsy is a chronic electrophysiological disorder characterized by repetitive seizures. Electroencephalogram (EEG) signals represent the dynamic condition of the brain. Variations in the EEG signals cannot be predicted by ocular examination. Thus, signal processing methods are used to extract the hidden information from the EEG signals. In this work, three different types of EEG signals (normal, epileptic, background) are used and features are extracted using wavelet entropies and energies. ANOVA is performed followed by classification. A graphical user interface (GUI) is introduced that can help in the automatic classification of the EEG signals. Various classifiers such as decision tree, k-nearest neighbour, support vector machine and fuzzy classifiers are used. Classification accuracy of 97% is achieved using decision tree classifier with GUI.
AB - Epilepsy is a chronic electrophysiological disorder characterized by repetitive seizures. Electroencephalogram (EEG) signals represent the dynamic condition of the brain. Variations in the EEG signals cannot be predicted by ocular examination. Thus, signal processing methods are used to extract the hidden information from the EEG signals. In this work, three different types of EEG signals (normal, epileptic, background) are used and features are extracted using wavelet entropies and energies. ANOVA is performed followed by classification. A graphical user interface (GUI) is introduced that can help in the automatic classification of the EEG signals. Various classifiers such as decision tree, k-nearest neighbour, support vector machine and fuzzy classifiers are used. Classification accuracy of 97% is achieved using decision tree classifier with GUI.
UR - http://www.scopus.com/inward/record.url?scp=84911453092&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84911453092&partnerID=8YFLogxK
U2 - 10.1166/jmihi.2014.1335
DO - 10.1166/jmihi.2014.1335
M3 - Article
VL - 4
SP - 868
EP - 873
JO - Journal of Medical Imaging and Health Informatics
JF - Journal of Medical Imaging and Health Informatics
SN - 2156-7018
IS - 6
ER -