This paper presents new class of time-frequency (T-F) features for automatic detection and classification of epileptic seizure activities in EEG signals. Most previous methods were based only on signal features derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. The proposed features based on image descriptors are extracted from the T-F representation of EEG signals and are considered and processed as an image using T-F image processing techniques. The proposed features include shape and texture-based descriptors and are able to describe visually the normal and seizure activity patterns observed in T-F images. The results obtained on real EEG data show that T-F image descriptor-based features achieve an overall classification accuracy of up to 98% for 100 EEG segments using one-against-one SVM classifier. The results suggest that the proposed method outperforms those methods, which employ signal features only or combined signal-image features by about 3% for 100 EEG signals.
|Publication status||Published - Apr 2015|
|Event||ICASSP 2015 - IEEE International Conference on Acoustics, Speech and Signal Processing - Brisbane, Australia|
Duration: 1 Apr 2015 → …
|Conference||ICASSP 2015 - IEEE International Conference on Acoustics, Speech and Signal Processing|
|Period||1/04/15 → …|