Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning

• Published in: Physiology & behavior 2016-10, Vol.165, p.413-424
• Main Authors: Jungheim, Michael, M.D, Busche, Andre, Ph.D, Miller, Simone, B.A. Linguistics, Schilling, Nicolas, Dipl.Math, Schmidt-Thieme, Lars, Ph.D, Ptok, Martin, M.D., Ph.D
• Format: Article
• Language: English
• Subjects: Adult; Deglutition; Deglutition - physiology; Dysphagia; Esophageal Sphincter, Upper - physiology; Female; Healthy Volunteers; High resolution manometry; Humans; Machine Learning; Machine learning model; Male; Manometry; Middle Aged; Pressure; Psychiatry; Restitution time; Retrospective Studies; Time Factors; Upper esophageal sphincter; Young Adult
• ISSN: 0031-9384; 1873-507X
• DOI: 10.1016/j.physbeh.2016.08.005

Abstract Objective After swallowing, the upper esophageal sphincter (UES) needs a certain amount of time to return from maximum pressure to the resting condition. Disturbances of sphincter function not only during the swallowing process but also in this phase of pressure restitution may lead to globus sensation or dysphagia. Since UES pressures do not decrease in a linear or asymptotic manner, it is difficult to determine the exact time when the resting pressure is reached, even when using high resolution manometry (HRM). To overcome this problem a Machine Learning model was established to objectively determine the UES restitution time (RT) and moreover to collect physiological data on sphincter function after swallowing. Methods and material HRM-data of 15 healthy participants performing 10 swallows each were included. After manual annotation of the RT interval by two swallowing experts, data were transferred to the Machine Learning model, which applied a sequence labeling modeling approach based on logistic regression to learn and objectivize the characteristics of all swallows. Individually computed RT values were then compared with the annotated values. Results Estimates of the RT were generated by the Machine Learning model for all 150 swallows. When annotated by swallowing experts mean RT of 11.16 s ± 5.7 (SD) and 10.04 s ± 5.74 were determined respectively, compared to model-generated values from 8.91 s ± 3.71 to 10.87 s ± 4.68 depending on model selection. The correlation score for the annotated RT of both examiners was 0.76 and 0.63 to 0.68 for comparison of model predicted values. Conclusions Restitution time represents an important physiologic swallowing parameter not previously considered in HRM-studies of the UES, especially since disturbances of UES restitution may increase the risk of aspiration. The data presented here show that it takes approximately 9 to 11 s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25–30 s in between swallows is necessary until the next swallow is initiated. This should be considered in any further HRM-studies designed to evaluate the characteristics of individual swallows. The calculation model enables a quick and reproducible determination of the time it takes for the UES to come to rest after swallowing (RT). The results of the calculation are partially independent of the input of the investigator. Adding more swallows and integrating additional param