Application of Classification Models to Pharyngeal High-Resolution Manometry

Abstract

The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were evaluated for their ability to identify disordered swallowing. Data were collected from 12 control subjects and 13 subjects with swallowing disorders; for this experiment, these subjects swallowed 5-ml water 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 networks. All methods produced high average classification accuracies, with MLP, SVM, and LVQ achieving accuracies of 96.44%, 91.03%, and 85.39%, respectively. When evaluating the individual contributions of each parameter and groups of parameters to the classification accuracy, parameters pertaining to the upper esophageal sphincter were most valuable. Classification models show high accuracy in segregating HRM data sets and represent 1 method of facilitating application of HRM to the clinical setting by eliminating the time required for some aspects of data extraction and interpretation.