Artificial Neural Network and Mobile Applications in Medical Diagnosis

Pearce, Gillian, Wong, Julian, Mirtskhulava, Lela, Al-Majeed, Salah, Bakuria, Koba and Gulua, Nana (2016) Artificial Neural Network and Mobile Applications in Medical Diagnosis. In: 2015 17th UKSim-AMSS International Conference on Modelling and Simulation, 25-27 March, Cambridge, UK.

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Abstract

The aim of this paper is to present a pilot study regarding the application of an ANN to stroke recognition and diagnosis. Our system makes use of a (i) a neural network that can be trained to recognize normal limb movements (for individual patients), which may then be coupled to (ii) a physical grid mattress that can be used in the patient's home. Any changes in the patient's movement could potentially indicate that stroke has occurred are transmitted to a mobile phone app. The latter in turn alerts a relative or ambulance to render rapid assistance to the individual. When stroke has occurred it is essential to transfer the patient to hospital very quickly in order that treatment can be given promptly. In the case of strokes that have arisen due to a blood clot in the cerebral circulation of the brain, a drug called Alteplase (an anti-thrombolytic) must be given within 4.5 hours of the stroke occurring to be maximally effective. Therefore it is important to know the exact time on stroke onset. Our system would record the time of onset of the stroke, by recognizing and recording abnormal changes in the individual's limb movements. A Feed forward neural network was used in our modeling.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Artificial network model; Stroke; Soft sensors; Mobile telemedicine systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Schools and Research Institutes > School of Business and Technology > Technical & Applied Computing
Research Priority Areas: Applied Business & Technology
Depositing User: Susan Turner
Date Deposited: 20 Apr 2018 15:58
Last Modified: 11 Sep 2018 12:21
URI: http://eprints.glos.ac.uk/id/eprint/5580

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