A methodology for time-frequency image processing applied to the classification of nonstationary multichannel signals using instantaneous frequency descriptors with application to newborn EEG signals
MetadataShow full item record
This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. The methodology combines methods adapted from three complementary areas: time-frequency signal analysis, multichannel signal analysis and image processing. The latter three combine in a new methodology referred to as multichannel time-frequency image processing which is applied to the problem of classifying electroencephalogram (EEG) abnormalities in both adults and newborns. A combination of signal related features and image related features are used by merging key instantaneous frequency descriptors which characterize the signal non-stationarities. The results obtained show that, firstly, the features based on time-frequency image processing techniques such as image segmentation, improve the performance of EEG abnormalities detection in the classification systems based on multi-SVM and neural network classifiers. Secondly, these discriminating features are able to better detect the correlation between newborn EEG signals in a multichannel-based newborn EEG seizure detection for the purpose of localizing EEG abnormalities on the scalp.
- Electrical Engineering [201 items ]
Showing items related by title, author, creator and subject.
|Time-frequency signal and image processing of non-stationary signals with application to the classification of newborn EEG abnormalities ||Boashash, Boualem; Boubchir, Larbi; Azemi, Ghasem||2011||IEEE||Conference Paper|
|Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy ||Sucic, Victor; Saulig, Nicoletta; Boashash, Boualem||2011||Springer||Article|
|An automatic time-frequency procedure for interference suppression by exploiting their geometrical features ||Saulig, Nicoletta; Sucic, Victor; Boashash, Boualem||2011||IEEE||Conference Paper|