Browsing by Author "Azemi, Ghasem"
Now showing items 1-9 of 9
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Automated detection of perinatal hypoxia using time-frequency-based heart rate variability features
Dong, Shiying; Boashash, Boualem; Azemi, Ghasem; Lingwood, Barbara E.; Colditz, Paul B. ( Springer Berlin Heidelberg , 2014 , Article)Perinatal hypoxia is a cause of cerebral injury in foetuses and neonates. Detection of foetal hypoxia during labour based on the pattern recognition of heart rate signals suffers from high observer variability and low ... -
Classification of fetal movement accelerometry through time-frequency features
Layeghy, Siamak; Azemi, Ghasem; Colditz, Paul; Boashash, Boualem ( IEEE , 2014 , Conference Paper)This paper presents a time-frequency approach for fetal movement monitoring which is based on classification of accelerometry signals collected from pregnant women's abdomen. Features extracted from time-frequency distribution ... -
Detection of neonatal EEG burst-suppression using a time-frequency approach
Awal, Md. Abdul; Colditz, Paul B.; Boashash, Boualem; Azemi, Ghasem ( IEEE , 2014 , Conference Paper)In newborn EEG, the presence of burst suppression carries with it a high probability of poor neurodevelopmental outcome. This paper presents a novel method to detect neonatal bust suppression from multichannel EEG using a ... -
EEG background features that predict outcome in term neonates with hypoxic ischaemic encephalopathy: A structured review
Awal, Md Abdul; Lai, Melissa M.; Azemi, Ghasem; Boashash, B.; Colditz, Paul B. ( Elsevier Ireland Ltd , 2016 , Article)Objectives Hypoxic ischaemic encephalopathy is a significant cause of mortality and morbidity in the term infant. Electroencephalography (EEG) is a useful tool in the assessment of newborns with HIE. This systematic review ... -
EEG-based automatic epilepsy diagnosis using the instantaneous frequency with sub-band energies
Fani, Mohammad; Azemi, Ghasem; Boashash, Boualem ( IEEE , 2011 , Conference Paper)This paper presents a novel approach for classifying the electroencephalogram (EEG) signals as normal or abnormal. This method uses features derived from the instantaneous frequency (IF) and energies of EEG signals in ... -
Measuring time-varying information flow in scalp EEG signals: Orthogonalized partial directed coherence
Omidvarnia, Amir; Azemi, Ghasem; Boashash, Boualem; Otoole, John M.; Colditz, Paul B.; Vanhatalo, Sampsa... more authors ... less authors ( IEEE , 2014 , Article)This study aimed to develop a time-frequency method for measuring directional interactions over time and frequency from scalp-recorded electroencephalographic (EEG) signals in a way that is less affected by volume conduction ... -
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. ( Springer , 2003 , Article)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 ... -
Surrogate data test for nonlinearity of EEG signals: A newborn EEG burst suppression case study
Mirzaei, Parisa; Azemi, Ghasem; Japaridze, Natia; Boashash, B. ( Elsevier Inc. , 2017 , Article)This paper applies the surrogate data method to investigate the presence of nonlinearity in neonatal electroencephalogram (EEG) burst suppression (B/S) patterns in order to rationalize the use of nonlinear methods for ... -
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 ...