M
Mostefa Mesbah
Researcher at University of Queensland
Publications - 133
Citations - 2022
Mostefa Mesbah is an academic researcher from University of Queensland. The author has contributed to research in topics: Electroencephalography & Signal. The author has an hindex of 24, co-authored 125 publications receiving 1883 citations. Previous affiliations of Mostefa Mesbah include Royal Brisbane and Women's Hospital & University of Western Australia.
Papers
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Journal ArticleDOI
IF estimation for multicomponent signals using image processing techniques in the time-frequency domain
TL;DR: The proposed IF estimator is tested on noisy synthetic monocomponent and multicomponent signals exhibiting linear and nonlinear laws and a classification method using least squares data-fitting is proposed and illustrated on synthetic and real signals.
Time-Frequency Feature Extraction of Newborne EEG Seizure using SVD-Based Techniques
TL;DR: In this article, a new time-frequency-based EEG seizure detection technique was proposed, which uses an estimate of the distribution function of the singular vectors associated with the timefrequency distribution of an EEG epoch to characterise the patterns embedded in the signal.
Journal ArticleDOI
Signal enhancement by time-frequency peak filtering
Boualem Boashash,Mostefa Mesbah +1 more
TL;DR: In this article, a class of signals for which the method implemented using the pseudo Wigner-Ville distribution (WVD) is approximately unbiased is characterized, and a pseudo WVD window length that gives a reduced bias when TFPF is used for signals from this class is derived.
Journal ArticleDOI
A Nonstationary Model of Newborn EEG
TL;DR: The results show that the output of the proposed models have a higher correlation with real newborn EEG than currently accepted models (a 10% and 38% improvement for background and seizure models, respectively)
Journal ArticleDOI
Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques
TL;DR: A new time-frequency-based EEG seizure detection technique that uses an estimate of the distribution function of the singular vectors associated with the time- frequency distribution of an EEG epoch to characterise the patterns embedded in the signal.