Liu Shuang, Xu Minpeng, Yang Jiajia, Qi Hongzhi, He Feng, Zhao Xin, Zhou Peng, Zhang Lixin, Ming Dong
Neural Engineering & Rehabilitation Lab, Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Comput Math Methods Med. 2017;2017:9239074. doi: 10.1155/2017/9239074. Epub 2017 Nov 14.
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.
动态24小时食管pH监测被认为是诊断胃食管反流病(GERD)的金标准,在临床应用中,静态参数如DeMeester评分被广泛使用。然而,这些静态变量的一个缺点是它们的假阴性率相对较高,且需要较长的记录时间。它们可能会被错误地标记为无反流者,从而得不到适当的治疗。因此,有必要寻找更准确、客观的参数来检测和量化GERD。本文首先介绍了一项新的研究,该研究探讨了24小时pH记录动态特征的可行性。对三组(重度、轻至中度和正常)进行了小波能量、信息熵和小波熵的估计。结果表明,小波能量和熵具有生理意义,因为它们能够区分不同程度GERD的患者。采用k均值聚类算法来获得新参数的敏感性和特异性。很明显,信息熵的敏感性最高,为87.3%,小波能量的特异性最高,为97.1%。这将有助于更准确地定义检测和量化GERD的最佳指标,并为GERD的早期诊断提供另一种思路。