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    AuthorPorter, Emily (7)
    Santorelli, Adam (7)
    O'Halloran, Martin (6)McGinley, Brian (4)McDermott, Barry (3)... View MoreSubjectABLATION (1)Biomedical applications (1)Biomedical engineering (1)Biomedical imaging (1)Bladder (1)... View MoreDate Issued2020 (1)2018 (4)2017 (2)TypeArticle (6)Conference Paper (1)

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    Brain haemorrhage detection using a SVM classifier with electrical impedance tomography measurement frames 

    McDermott, Barry; O'Halloran, Martin; Porter, Emily; Santorelli, Adam (Public Library of Science, 2018-07-12)
    Brain haemorrhages often require urgent treatment with a consequent need for quick and accurate diagnosis. Therefore, in this study, we investigate Support Vector Machine (SVM) classifiers for detecting brain haemorrhages ...
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    Classification applied to brain haemorrhage detection: Initial phantom studies using electrical impedance measurements 

    McDermott, Barry; O'Halloran, Martin; Santorelli, Adam; McGinley, Brian; Porter, Emily (EIT2018, 2018-06-11)
    Machine learning and classification algorithms are applied to data collected from an EIT system. The system is used with an anatomically accurate head phantom setup in a variety of situations modelling normal and haemorrhagic ...
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    Anatomically and dielectrically realistic microwave head phantom with circulation and reconfigurable lesions 

    McDermott, Barry; Porter, Emily; Santorelli, Adam; Divilly, Brendan; Morris, Liam; Jones, Marggie; McGinley, Brian; O'Halloran, Martin (The Electromagnetics Academy, 2017)
    Phantoms provide valuable test platforms for developing medical devices. Solid materials in particular allow fabrication of stable and robust models. This paper presents a novel, anatomically realistic, multi-layered head ...
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    Supervised learning classifiers for electrical impedance-based bladder state detection 

    Dunne, Eoghan; Santorelli, Adam; McGinley, Brian; Leader, Geraldine; O’Halloran, Martin; Porter, Emily (Nature Publishing Group, 2018-03-29)
    Urinary Incontinence affects over 200 million people worldwide, severely impacting the quality of life of individuals. Bladder state detection technology has the potential to improve the lives of people with urinary ...
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    Modeling of the dielectric properties of biological tissues within the histology region 

    Porter, Emily; La Gioia, Alessandra; Santorelli, Adam; O'Halloran, Martin (Institute of Electrical and Electronics Engineers (IEEE), 2017-11-27)
    The dielectric properties of biological tissues characterize the interaction of electromagnetic fields with the human body. As such, accurate knowledge of these properties is vital to the design and development of ...
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    Image-based classification of bladder state using electrical impedance tomography 

    Dunne, Eoghan; Santorelli, Adam; McGinley, Brian; Leader, Geraldine; O'Halloran, Martin; Porter, Emily (IOP Publishing, 2018-12-03)
    Objective: In this study, we examine the potential of using machine learning classification to determine the bladder state ( not full , full ) with electrical impedance tomography (EIT) images of the pelvic region. Accurate ...
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    Predicting the sensing radius of a coaxial probe based on the probe dimensions 

    La Gioia, Alessandra; Santorelli, Adam; O'Halloran, Martin; Porter, Emily (Institute of Electrical and Electronics Engineers, 2020-05-19)
    The coaxial probe technique is used to acquire the dielectric properties of biological tissues in the microwave frequency range. To dielectrically characterize heterogeneous samples, the sensing radius of the probe must ...
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