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Stochastic relevance analysis of epileptic EEG signals for channel selection and classification
- Source :
- EMBC
- Publication Year :
- 2013
- Publisher :
- IEEE, 2013.
-
Abstract
- Time-frequency decompositions (TFDs) are well known techniques that permit to extract useful information or features from EEG signals, being necessary to distinguish between irrelevant information and the features effectively representing the subjacent physiological phenomena, according to some evaluation measure. This work introduces a new method to obtain relevant features extracted from time-frequency plane for epileptic EEG signals. Particularly, EEG features are extracted by common spectral methods such as short time Fourier transform (STFT), wavelets transform and Empirical Mode Decomposition (EMD). Then, each method is evaluated by Stochastic Relevance Analysis (SRA) that is further used for EEG classification and channel selection. The classification measures are carried out based on the performance of the k-NN classifier, while the channels selected are validated by visual inspection and topographic scalp map. The study uses real and multi-channel EEG data and all the experiments have been supervised by an expert neurologist. Results obtained in this paper show that SRA is a good alternative for automatic seizure detection and also opens the possibility of formulating new criteria to select, classify or analyze abnormal EEG channels.
- Subjects :
- Adult
Computer science
Speech recognition
Feature extraction
Electroencephalography
Hilbert–Huang transform
symbols.namesake
Wavelet
medicine
Humans
Stochastic Processes
Epilepsy
Fourier Analysis
medicine.diagnostic_test
business.industry
Short-time Fourier transform
Wavelet transform
Signal Processing, Computer-Assisted
Pattern recognition
Fourier transform
Fourier analysis
symbols
Artificial intelligence
Spectral method
business
Classifier (UML)
Algorithms
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
- Accession number :
- edsair.doi.dedup.....47f71da796af55c39bae88b3dd9489a4