Effectiveness of combined time-frequency imageand signal-based features for improving the detection and classification of epileptic seizure activities in EEG signals
This paper presents new time-frequency (T-F) features to improve the 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 generated from different spectral sub-bands. The proposed features are based on T-F image descriptors, which are extracted from the T-F representation of EEG signals, are considered and processed as an image using image processing techniques. The idea of the proposed feature extraction method is based on the application of Otsu's thresholding algorithm on the T-F image in order to detect the regions of interest where the epileptic seizure activity appears. The proposed T-F image related-features are then defined to describe the statistical and geometrical characteristics of the detected regions. The results obtained on real EEG data suggest that the use of T-F image based-features with signal related-features improve significantly the performance of the EEG seizure detection and classification by up to 5% for 120 EEG signals, using a multi-class SVM classifier.
- Computer Science & Engineering [206 items ]
Showing items related by title, author, creator and subject.
|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 ||Azemi, Ghasem; Boubchir, Larbi; Boashash, B.||2003||Springer||Article|
|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|
|Refining the ambiguity domain characteristics of non-stationary signals for improved time�frequency analysis: Test case of multidirectional and multicomponent piecewise LFM and HFM signals ||BoashashB.; JawadB.K.; OuelhaS.||2018||Elsevier Inc.||Article|