Improving the classification of newborn EEG time-frequency representations using a combined time-frequency signal and image approach

This paper presents new time-frequency (T-F) features to improve the classification of non-stationary signals such as EEG signals. Previous methods were based only on signal features that were derived from the instantaneous frequency and energies of EEG signals in different spectral sub-bands. This paper includes new features that are based on T-F image descriptors which are extracted from the T-F representation considered as an image, using T-F image processing techniques. The results obtained on newborn EEG data, show that the use of image related-features with signal based-features improve the performance of the newborn EEG seizure detection and classification when using multi-SVM classifiers. These results allow the possibility of improving health outcomes for sick babies by early intervention on the basis of the results of the classification of newborn EEG abnormalities.