A manometric feature descriptor with linear-SVM to distinguish esophageal contraction vigor

• URL: http://arxiv.org/abs/2311.15609v1
• Date: Mon, 27 Nov 2023 08:06:56 GMT
• Title: A manometric feature descriptor with linear-SVM to distinguish esophageal contraction vigor
• Authors: Jialin Liu, Lu Yan, Xiaowei Liu, Yuzhuo Dai, Fanggen Lu, Yuanting Ma, Muzhou Hou, Zheng Wang
• Abstract summary: After the results of high-resolution manometry are obtained, doctors still need to evaluate by a variety of parameters. We conducted image processing of HRM to predict the esophageal contraction vigor for assisting the evaluation of esophageal dynamic function. Our accuracy reaches 86.83%, which is higher than other common machine learning methods.
• Score: 8.39812027404057
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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