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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 9239074, 7 pages
https://doi.org/10.1155/2017/9239074
Research Article

Research on Gastroesophageal Reflux Disease Based on Dynamic Features of Ambulatory 24-Hour Esophageal pH Monitoring

Neural Engineering & Rehabilitation Lab, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China

Correspondence should be addressed to Dong Ming; nc.ude.ujt@gnimdrahcir

Received 15 March 2017; Revised 25 July 2017; Accepted 1 August 2017; Published 14 November 2017

Academic Editor: Emil Alexov

Copyright © 2017 Shuang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Ambulatory 24-hour esophageal pH monitoring has been considered as the gold standard for diagnosing gastroesophageal reflux disease (GERD), and in clinical application, static parameters are widely used, such as DeMeester score. However, a shortcoming of these static variables is their relatively high false negative rate and long recording time required. They may be falsely labeled as nonrefluxers and not appropriately treated. Therefore, it is necessary to seek more accurate and objective parameters to detect and quantify GERD. This paper first describes a new effort that investigated the feasibility of dynamic features of 24-hour pH recording. Wavelet energy, information entropy, and wavelet entropy were estimated for three groups (severe, mild-to-moderate, and normal). The results suggest that wavelet energy and entropy are physiologically meaningful since they differentiated patients with varying degrees of GERD. -means clustering algorithm was employed to obtain the sensitivity and specificity of new parameters. It is obvious that information entropy goes with the highest sensitivity of 87.3% and wavelet energy has the highest specificity of 97.1%. This would allow a more accurate definition of the best indicators to detect and quantify GERD as well as provide an alternative insight into the early diagnosis of GERD.