Swallowing function evaluation using deep-learning-based acoustic signal processing

Chisa Kodama, Kunihito Kato, Satoshi Tamura, Satoru Hayamizu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In recent years, people with swallowing disorder are increasing. Therefore, it is important to evaluate the swallowing function in the early detection and prevention of swallowing disorder. In this study, we used a capsule that generates sound and estimated the timing at which food is sent to the esophagus by sound signal processing and deep learning. By comparing it with the movement of the epiglottis tracked by the image, we performed a noninvasive and quantitative swallowing function evaluation.

Original languageEnglish
Title of host publicationProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages961-964
Number of pages4
ISBN (Electronic)9781538615423
DOIs
Publication statusPublished - 2018 Feb 5
Externally publishedYes
Event9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia
Duration: 2017 Dec 122017 Dec 15

Publication series

NameProceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume2018-February

Other

Other9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
CountryMalaysia
CityKuala Lumpur
Period17/12/1217/12/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Information Systems
  • Signal Processing

Fingerprint Dive into the research topics of 'Swallowing function evaluation using deep-learning-based acoustic signal processing'. Together they form a unique fingerprint.

Cite this