Title | ||
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Allergy detection with statistical modelling of HRV-based non-reaction baseline features |
Abstract | ||
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This paper investigates the automated classification of oral food challenges ('allergy tests'). The electrocardiograms (ECG) of the subjects being tested for allergies were recorded via a wireless mote, and the QRS complexes were manually annotated and 18 features were extracted from the signals. Principal component analysis was used for feature decorelation and dimensionality reduction and diagonal covariance Gaussian mixture models were used to model non-reaction baseline patient condition. The generated subject independent log likelihood plots were used to separate allergic reaction by means of subject adaptive thresholding. The platform resulted in 87% accuracy of classification with 100% specificity. The algorithm presented can detect allergy up to 30 minutes sooner than the current state of the clinical art allergy detection (7minutes ± 9). |
Year | DOI | Venue |
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2011 | 10.1145/2093698.2093832 | ISABEL |
Keywords | Field | DocType |
statistical modelling,automated classification,allergy test,allergic reaction,hrv-based non-reaction baseline feature,diagonal covariance gaussian mixture,subject independent log likelihood,subject adaptive thresholding,clinical art allergy detection,qrs complex,dimensionality reduction,current state,principal component analysis,space diversity,adaptive thresholding,gaussian mixture model,ultra wideband | Dimensionality reduction,Telecommunications,Computer science,QRS complex,Artificial intelligence,Thresholding,Allergy,Covariance,Computer vision,Pattern recognition,Statistical model,Principal component analysis,Mixture model | Conference |
Citations | PageRank | References |
2 | 0.41 | 0 |
Authors | ||
4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Niall Twomey | 1 | 76 | 11.06 |
Andrey Temko | 2 | 271 | 22.04 |
Jonathan O'B. Hourihane | 3 | 2 | 0.41 |
William P. Marnane | 4 | 427 | 41.38 |