IEEE J Biomed Health Inform - Automated detection of perturbed cardiac physiology during oral food allergen challenge in children.

Tópicos

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Resumo

This paper investigates the fully automated computer-based detection of allergic reaction in oral food challenges using pediatric ECG signals. Nonallergic background is modeled using a mixture of Gaussians during oral food challenges, and the model likelihoods are used to determine whether a subject is allergic to a food type. The system performance is assessed on the dataset of 24 children (15 allergic and 9 nonallergic) totaling 34 h of data. The proposed detector correctly classified all nonallergic subjects (100% specificity) and 12 allergic subjects (80% sensitivity) and is capable of detecting allergy on average 17 min earlier than trained clinicians during oral food challenges, the gold standard of allergy diagnosis. Inclusion of the developed allergy classification platform during oral food challenges recorded would result in a 30% reduction of doses administered to allergic subjects. The results of study introduce the possibility to halt challenges earlier which can safely advance the state of clinical art of allergy diagnosis by reducing the overall exposure to the allergens.

Resumo Limpo

paper investig fulli autom computerbas detect allerg reaction oral food challeng use pediatr ecg signal nonallerg background model use mixtur gaussian oral food challeng model likelihood use determin whether subject allerg food type system perform assess dataset children allerg nonallerg total h data propos detector correct classifi nonallerg subject specif allerg subject sensit capabl detect allergi averag min earlier train clinician oral food challeng gold standard allergi diagnosi inclus develop allergi classif platform oral food challeng record result reduct dose administ allerg subject result studi introduc possibl halt challeng earlier can safe advanc state clinic art allergi diagnosi reduc overal exposur allergen

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