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Pearce, Gillian, Wong, Julian, Mirtskhulava, Lela, Al-Majeed, Salah 
ORCID: 0000-0002-5932-9658, 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.
ISBN 978-1-4799-8713-9
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Text (Peer reviewed version)
5580 Al-Majeed (2015) Artificial Neural Network and Mobile Applications in Medical Diagnosis.pdf - Accepted Version Available under License All Rights Reserved. Download (189kB) | Preview |
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, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Susan Turner |
| Date Deposited: | 20 Apr 2018 15:58 |
| Last Modified: | 31 Aug 2023 08:01 |
| URI: | https://eprints.glos.ac.uk/id/eprint/5580 |
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