Michael Jungheim1, Andre Busche2, Simone Miller3, Nicolas Schilling4, Lars Schmidt-Thieme5, Martin Ptok6. 1. Department of Phoniatrics and Pediatric Audiology, Hannover Medical School, Germany. Electronic address: jungheim.michael@mh-hannover.de. 2. Brunel GmbH, Bereich Communications, Daimlerring 9, 31135 Hildesheim, Germany; Information Systems and Machine Learning Lab, University of Hildesheim, Germany. Electronic address: busche@ismll.uni-hildesheim.de. 3. Department of Phoniatrics and Pediatric Audiology, Hannover Medical School, Germany. Electronic address: miller.simone@mh-hannover.de. 4. Information Systems and Machine Learning Lab, University of Hildesheim, Germany. Electronic address: schilling@ismll.uni-hildesheim.de. 5. Information Systems and Machine Learning Lab, University of Hildesheim, Germany. Electronic address: schmidt-thieme@ismll.uni-hildesheim.de. 6. Department of Phoniatrics and Pediatric Audiology, Hannover Medical School, Germany. Electronic address: ptok.martin@mh-hannover.de.
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 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±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 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s 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 parameters will improve the Machine Leaning model in the future. By applying similar models to other swallowing parameters of the pharynx and UES, such as the relaxation time of the UES or the activity time during swallowing, a complete automatic evaluation of HRM-data of a swallow should be possible.
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 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±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 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s 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 parameters will improve the Machine Leaning model in the future. By applying similar models to other swallowing parameters of the pharynx and UES, such as the relaxation time of the UES or the activity time during swallowing, a complete automatic evaluation of HRM-data of a swallow should be possible.
Authors: Vatsal Patel; Marium N Khan; Aman Shrivastava; Kamran Sadiq; S Asad Ali; Sean R Moore; Donald E Brown; Sana Syed Journal: J Pediatr Gastroenterol Nutr Date: 2020-01 Impact factor: 3.288