Skip to Content
Authors Hoffman MR, Mielens JD, Omari TI, Rommel N, Jiang JJ, McCulloch TM
Author Profile(s)
Lab(s)
Journal Laryngoscope Volume: 123 Issue: 3 Pages: 713-20
Publish Date 2013 Mar
PubMed ID 23070810
PMC ID 3648989
Abstract

To use classification algorithms to classify swallows as safe, penetration, or aspiration based on measurements obtained from pharyngeal high-resolution manometry (HRM) with impedance.Case series evaluating new method of data analysis.Multilayer perceptron, an artificial neural network (ANN), was evaluated for its ability to classify swallows as safe, penetration, or aspiration. Data were collected from 25 disordered subjects swallowing 5- or 10-mL boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the ANN.A classification accuracy of 89.4 ± 2.4% was achieved when including all parameters. Including only manometry-related parameters yielded a classification accuracy of 85.0 ± 6.0%, whereas including only impedance-related parameters yielded a classification accuracy of 76.0 ± 4.9%. Receiver operating characteristic analysis yielded areas under the curve of 0.8912 for safe, 0.8187 for aspiration, and 0.8014 for penetration.Classification models show high accuracy in classifying swallows from dysphagic patients as safe or unsafe. HRM-impedance with ANN represents one method that could be used clinically to screen for patients at risk for penetration or aspiration.

Full Text Full text available on PubMed Central
webmaster@surgery.wisc.edu Copyright © 2016 The Board of Regents of the University of Wisconsin System