Calibration of time features and frequency features in the time-frequency domain for improved detection and classification of seizure in newborn EEG signals
This paper presents new time-frequency features for seizure detection in newborn EEG signals. These features are obtained by calibrating relevant time features and frequency features in the joint time-frequency domain. The proposed features allow the possibility of improving the performance of the seizure detection and classification system based on multi-class SVM classifier.
- Electrical Engineering [2383 items ]
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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 ( IEEE , 2011 , Conference Paper)This paper presents an introduction to time-frequency (T-F) methods in signal processing, and a novel approach for EEG abnormalities detection and classification based on a combination of signal related features and image ...
Boashash B.; Khan N.A.; Ben-Jabeur T. ( Elsevier Inc. , 2015 , Article)This paper presents a tutorial review of recent advances in the field of time-frequency (t, f) signal processing with focus on exploiting (t, f) image feature information using pattern recognition techniques for detection ...
Estimating the number of components of a multicomponent nonstationary signal using the short-term time-frequency Rényi entropy Sucic, Victor; Saulig, Nicoletta; Boashash, Boualem ( Springer , 2011 , Article)The time-frequency Rényi entropy provides a measure of complexity of a nonstationary multicomponent signal in the time-frequency plane. When the complexity of a signal corresponds to the number of its components, then this ...