ORCID Profile
0000-0002-8675-6858
Current Organisations
Razi University
,
Macquarie University
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Publisher: IEEE
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2004
Publisher: IEEE
Date: 05-2011
Publisher: Elsevier BV
Date: 05-2014
Publisher: IEEE
Date: 2003
Publisher: Elsevier BV
Date: 2019
Publisher: Elsevier BV
Date: 05-2013
Publisher: Elsevier BV
Date: 2021
Publisher: IEEE
Date: 2005
Publisher: Elsevier
Date: 2016
Publisher: Elsevier BV
Date: 06-2021
Publisher: IEEE
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2014
Publisher: Elsevier BV
Date: 03-2015
Publisher: IEEE
Date: 07-2012
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer Science and Business Media LLC
Date: 22-05-2017
Publisher: IEEE
Date: 12-2011
Publisher: IEEE
Date: 2003
Publisher: Springer Science and Business Media LLC
Date: 29-05-2012
DOI: 10.1186/1687-6180-2012-117
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.
Publisher: Elsevier BV
Date: 2016
DOI: 10.1016/J.CLINPH.2015.05.018
Abstract: 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 of published literature identifies those background features of EEG in term neonates with HIE that best predict neurodevelopmental outcome. A literature search was conducted using the PubMed, EMBASE and CINAHL databases from January 1960 to April 2014. Studies included in the review described recorded EEG background features, neurodevelopmental outcomes at a minimum age of 12 months and were published in English. Pooled sensitivities and specificities of EEG background features were calculated and meta-analyses were performed for each background feature. Of the 860 articles generated by the initial search strategy, 52 studies were identified as potentially relevant. Twenty-one studies were excluded as they did not distinguish between different abnormal background features, leaving 31 studies from which data were extracted for the meta-analysis. The most promising neonatal EEG features are: burst suppression (sensitivity 0.87 [95% CI (0.78-0.92)] specificity 0.82 [95% CI (0.72-0.88)]), low voltage (sensitivity 0.92 [95% CI (0.72-0.97)] specificity 0.99 [95% CI (0.88-1.0)]), and flat trace (sensitivity 0.78 [95% CI (0.58-0.91)] specificity 0.99 [95% CI (0.88-1.0)]). Burst suppression, low voltage and flat trace in the EEG of term neonates with HIE most accurately predict long term neurodevelopmental outcome. This structured review and meta-analysis provides quality evidence of the background EEG features that best predict neurodevelopmental outcome.
Publisher: Springer Science and Business Media LLC
Date: 19-05-2021
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 09-2009
Publisher: Elsevier BV
Date: 11-2013
Publisher: Wiley
Date: 10-2009
DOI: 10.1002/WCM.725
Publisher: Springer Science and Business Media LLC
Date: 21-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2004
Publisher: IEEE
Date: 02-2007
Publisher: Elsevier BV
Date: 07-2021
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: IEEE
Date: 05-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2013
Publisher: IEEE
Date: 09-2009
Publisher: Elsevier BV
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 11-09-2014
Publisher: IEEE
Date: 03-2012
Publisher: IEEE
Date: 2001
Publisher: IEEE
Date: 11-2018
Publisher: IEEE
Date: 05-2014
Location: Iran (Islamic Republic of)
No related grants have been discovered for Ghasem Azemi.