Digital signal processing and artificial intelligence for the automated classification of food allergy

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dc.contributor.advisor Marnane, William P. en
dc.contributor.author Twomey, Niall Joseph
dc.date.accessioned 2013-09-25T16:00:40Z
dc.date.available 2013-09-25T16:00:40Z
dc.date.issued 2013
dc.date.submitted 2013
dc.identifier.citation Twomey, N.J. 2013. Digital signal processing and artificial intelligence for the automated classification of food allergy. PhD Thesis, University College Cork. en
dc.identifier.endpage 251
dc.identifier.uri http://hdl.handle.net/10468/1236
dc.description.abstract As a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated and intelligent interpretation of data. This is particularly true in medical and clinical settings, where research into machine-assisted diagnosis of physiological conditions gains momentum daily. Of the conditions which have been addressed, however, automated classification of allergy has not been investigated, even though the numbers of allergic persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences. On the basis of the observations of allergists who conduct oral food challenges (OFCs), activity-based analyses of allergy tests were performed. Algorithms were investigated and validated by a pilot study which verified that accelerometer-based inquiry of human movements is particularly well-suited for objective appraisal of activity. However, when these analyses were applied to OFCs, accelerometer-based investigations were found to provide very poor separation between allergic and non-allergic persons, and it was concluded that the avenues explored in this thesis are inadequate for the classification of allergy. Heart rate variability (HRV) analysis is known to provide very significant diagnostic information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS annotations, and these are not a viable methodology for real-time diagnostic applications. Even so, this was the first work which has categorically correlated changes in HRV features to the onset of allergic events, and manual annotations yield undeniable affirmation of this. Fostered by the successful results which were obtained with manual classifications, automatic QRS detection algorithms were investigated to facilitate the fully automated classification of allergy. The results which were obtained by this process are very promising. Most importantly, the work that is presented in this thesis did not obtain any false positive classifications. This is a most desirable result for OFC classification, as it allows complete confidence to be attributed to classifications of allergy. Furthermore, these results could be particularly advantageous in clinical settings, as machine-based classification can detect the onset of allergy which can allow for early termination of OFCs. Consequently, machine-based monitoring of OFCs has in this work been shown to possess the capacity to significantly and safely advance the current state of clinical art of allergy diagnosis en
dc.description.sponsorship Science Foundation Ireland (SFI Grant SFI/07/SRC/I1169) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2013, Niall J. Twomey en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Machine learning en
dc.subject Digital signal processing en
dc.subject Allergy en
dc.subject Heart rate variability en
dc.subject Automatic decision making en
dc.subject Automated classification of allergy en
dc.subject.lcsh Signal processing--Digital techniques en
dc.subject.lcsh Allergy--Diagnosis en
dc.subject.lcsh Food allergy en
dc.title Digital signal processing and artificial intelligence for the automated classification of food allergy en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PHD (Engineering) en
dc.internal.availability Full text available en
dc.check.info No embargo required en
dc.description.version Accepted Version
dc.contributor.funder Technology Research for Independent Living Centre, Mercer's Institute for Successful Ageing, St James’s Hospital, Dublin en
dc.contributor.funder Science Foundation Ireland en
dc.contributor.funder Irish Research Council for Science Engineering and Technology en
dc.description.status Not peer reviewed en
dc.internal.school Electrical and Electronic Engineering en
dc.check.type No Embargo Required
dc.check.reason No embargo required en
dc.check.opt-out No en
dc.thesis.opt-out false
dc.check.embargoformat Not applicable en
ucc.workflow.supervisor l.marnane@ucc.ie
dc.internal.conferring Autumn Conferring 2013 en


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