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

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

Pedem referens: art historical memory and the analogue in the work of Tacita Dean, Jeremy Millar and Lucy Skaer

This thesis explores the new art historical turn in contemporary art through close engagement with three British artworks. These are Tacita Dean’s, Section Cinema (Homage to Marcel Broodthaers), 2002, Jeremy Millar’s, The Man Who Looked Back, 2010, and Lucy Skaer’s, Leonora, 2006. Each of these artworks combines an art historical agenda with a celebration of the specificities of analogue film and photography in the context of our digital age. This thesis combines twentieth century photographic theory from Roland Barthes, André Bazin and Walter Benjamin, among others, with the psychoanalytic theory of Jacques Lacan in order to argue that the indexical qualities of analogue film and photography place the medium in close proximity to the Lacanian Real. In its obsolescence the analogue’s language of both touch and loss is heightened. Each chapter of this thesis explores a different aspect of the Real in relation to specific attributes of the analogue, such as its propensity for archiving cultural traumas, its receptiveness to chance, and its proximity to death.