Event Prediction in Pharyngeal High-Resolution Manometry

Abstract

A prolonged phase of increased pressure in the upper esophageal sphincter (UES) after swallowing might result in globus sensation. Therefore, it is important to evaluate restitution times of the UES in order to distinguish physiologic from impaired swallow associated activities. Estimating the event $t^{\star }$ where the UES has returned to its resting pressure after swallowing can be accomplished by predicting if swallowing activities are present or not. While the problem, whether a certain swallow is pathologic or not, is approached in Mielens (J Speech Lang Hear Res 55:892–902, 2012), the analysis conducted in this paper advances the understanding of normal pharyngoesophageal activities.

From the machine learning perspective, the problem is treated as binary sequence labeling, aiming to find a sample $t^{\star }$ within the sequence obeying a certain characteristic: We strive for a best approximation of label transition which can be understood as a dissection of the sequence into individual parts. Whereas common models for sequence labeling are based on graphical models (Nguyen and Guo, Proceedings of the 24th International Conference on Machine Learning. ACM, New York, pp. 681–688, 2007), we approach the problem using a logistic regression as classifier, integrate sequential features by means of FFT-coefficients and a Laplacian regularizer in order to encourage a smooth classification due to the monotonicity of target labels.