A manometric feature descriptor with linear-SVM to distinguish
  esophageal contraction vigor
Jialin Liu Lu Yan Xiaowei Liu Yuzhuo Dai Fanggen Lu Yuanting Ma Muzhou Hou Zheng Wang
n clinical, if a patient presents with nonmechanical obstructive dysphagia,
esophageal chest pain, and gastro esophageal reflux symptoms, the physician
will usually assess the esophageal dynamic function. High-resolution manometry
(HRM) is a clinically commonly used technique for detection of esophageal
dynamic function comprehensively and objectively. However, after the results of
HRM are obtained, doctors still need to evaluate by a variety of parameters.
This work is burdensome, and the process is complex. We conducted image
processing of HRM to predict the esophageal contraction vigor for assisting the
evaluation of esophageal dynamic function. Firstly, we used Feature-Extraction
and Histogram of Gradients (FE-HOG) to analyses feature of proposal of swallow
(PoS) to further extract higher-order features. Then we determine the
classification of esophageal contraction vigor normal, weak and failed by using
linear-SVM according to these features. Our data set includes 3000 training
sets, 500 validation sets and 411 test sets. After verification our accuracy
reaches 86.83%, which is higher than other common machine learning methods.