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Automated identification system for seizure EEG signals using tunable-Q wavelet transform
- Source :
- Engineering Science and Technology, an International Journal, Vol 20, Iss 5, Pp 1486-1493 (2017)
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- In the present work, EEG signals of different classes are analysed in tunable-Q wavelet transform (TQWT) framework. The TQWT decomposes the EEG signals into subbands and arrange them into decreasing order of frequency. The nonlinearity of the EEG signals is assessed by computing the centered correntropy (CCE) from the obtained subbands, which is further used as a feature for classifying the different categories of EEG signals. In this work, EEG signals are categorised in two different classification problems. First category is seizure free and seizure (NF-S) classes, and the other one is the normal, seizure free and seizure (ZO-NF-S) classes. Features obtained from the EEG signals of these classes are fed to the input of three different classifiers namely, random forest classifier (RF), multilayer perceptron (MLP) classifier, and logistic regression (LR) classifier. For NF-S classes, we achieved 98.3% classification accuracy with RF classifier for signal length of 1000 samples. The obtained accuracy of classification is 98.2% for ZO-NF-S classes using MLP classifier when features are extracted from signal length of 1000 samples.
- Subjects :
- Computer Networks and Communications
Speech recognition
02 engineering and technology
Electroencephalography
Identification system
Biomaterials
0202 electrical engineering, electronic engineering, information engineering
medicine
TQWT
Seizure activity
Civil and Structural Engineering
Mathematics
Fluid Flow and Transfer Processes
Classifiers
medicine.diagnostic_test
business.industry
Mechanical Engineering
Metals and Alloys
Wavelet transform
020206 networking & telecommunications
Pattern recognition
Centered correntropy
Seizure
Electronic, Optical and Magnetic Materials
Random forest
Electroencephalogram
lcsh:TA1-2040
Hardware and Architecture
Multilayer perceptron
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Engineering (General). Civil engineering (General)
business
Classifier (UML)
Subjects
Details
- ISSN :
- 22150986
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- Engineering Science and Technology, an International Journal
- Accession number :
- edsair.doi.dedup.....da55f0d6a9c1a3eaa6f48d39dc1709e7