Abstract
Abstract 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.
Highlights
This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization
5 Experimental results and discussion This section assesses the performance of the EEG abnormalities detection and localization schemes using the T-F image processing techniques, defined in the previous section in terms of features selection and extraction, localization and classification
The approach is illustrated on the key problem of newborn EEG abnormality diagnostic and localization for which a solution would help improve health outcomes for newborns
Summary
This article presents a general methodology for processing non-stationary signals for the purpose of classification and localization. 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 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. Instead, it is desired to get the EEG signal parameters extracted and analyzed using computer based digital signal processing (DSP) techniques Such an approach is highly useful in diagnostics and more suitable for automatic EEG abnormalities detection and classification [6,7]. This abnormality detection and localization problem can be solved by analyzing newborn EEG signals and extracting features, which are classified
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