Výsledky vyhledávání - "Yasser Al Hajjar"

Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals

Autor: Yasser Al Hajjar, Abd El Salam Ahmad Al Hajjar, Bassam Daya, Pierre Chauvet

The aim of this paper is to find the best intelligent model that allows predicting the future of premature newborns according to their electroencephalogram (EEG). EEG is a signal that measures the electrical activity of the brain. In this paper, the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d4b34dbe98d2856d2406f2829bdbc926
https://doi.org/10.4018/978-1-7998-2460-2.ch043

Zobrazit plný text záznamu

Determinant characteristics in EEG signal based on bursts amplitude segmentation for predicting pathological outcomes of a premature newborn, with validation using ANN

Autor: Yasser Al Hajjar, Bassam Daya, Abd El Salam Al Hajjar, Pierre Chauvet

Publikováno v: Analog Integrated Circuits and Signal Processing
Analog Integrated Circuits and Signal Processing, Springer Verlag, 2018, 96 (2), pp.243-251. ⟨10.1007/s10470-018-1129-1⟩

International audience; EEG signal contains some specific patterns that predict neuro-developmental impairments of a premature newborn. Extracting these patterns from a set of EEG records provides a dataset to be used in machine learning in order to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4c8e72015c0d704cbb610994f52c37e
https://doi.org/10.1007/s10470-018-1129-1

Zobrazit plný text záznamu

Intelligent Models to Predict the Prognosis of Premature Neonates According to Their EEG Signals

Autor: Bassam Daya, Pierre Chauvet, Abd El Salam Al Hajjar, Yasser Al Hajjar

Publikováno v: International Journal of Biomedical and Clinical Engineering. 6:57-66

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::42f210a025b64307d8a154b83ff9518b
https://doi.org/10.4018/ijbce.2017010105

Zobrazit plný text záznamu

Determinant characteristics in EEG signal based on bursts amplitude segmentation for predicting pathological outcomes of a premature newborn

Autor: Pierre Chauvet, Bassam Daya, Yasser Al Hajjar, Abd El Salam Al Hajjar

Publikováno v: Sensors Networks Smart and Emerging Technologies, SENSET 2017
Sensors Networks Smart and Emerging Technologies, SENSET 2017, Sep 2017, Beirut, Lebanon. ⟨10.1109/SENSET.2017.8125024⟩

Date Added to IEEE Xplore: 01 December 2017 Electronic ISBN: 978-1-5090-6011-5Print on Demand(PoD) ISBN: 978-1-5090-6012-2INSPEC Accession Number: 17379658; International audience; EEG signal contains some specific patterns that predict neuro-develop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ab39b8f73735f2b14f189b791f0dcad2
https://hal.univ-angers.fr/hal-02512164

Zobrazit plný text záznamu

Predication of premature neonates prognosis based on their electroencephalogram using artificial neural network

Autor: Yasser Al Hajjar, Bassam Daya, Pierre Chauvet, Abd El Salam Al Hajjar

Publikováno v: 2015 SAI Intelligent Systems Conference (IntelliSys).

The electroencephalogram (EEG) is a signal that measures the electrical activity of the brain. In this paper, we proposed an artificial neural network (ANN) having as output the category of the newborn (healthy, sick or risky) and as input 14 paramet

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bc740f52ef38bbc510323e8fb906c031
https://doi.org/10.1109/intellisys.2015.7361190

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání