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

Chisa Kodama; Kunihito Kato; Satoshi Tamura; Satoru Hayamizu

doi:10.1109/APSIPA.2017.8282161

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 proceeding › Conference 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 language	English
Title of host publication	Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	961-964
Number of pages	4
ISBN (Electronic)	9781538615423
DOIs	https://doi.org/10.1109/APSIPA.2017.8282161
Publication status	Published - 2017 Jul 2
Externally published	Yes
Event	9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 - Kuala Lumpur, Malaysia Duration: 2017 Dec 12 → 2017 Dec 15

Publication series

Name	Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Volume	2018-February

Other

Other	9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017
Country/Territory	Malaysia
City	Kuala Lumpur
Period	17/12/12 → 17/12/15

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Information Systems
Signal Processing

Access to Document

10.1109/APSIPA.2017.8282161

Cite this

Kodama, C., Kato, K., Tamura, S., & Hayamizu, S. (2017). Swallowing function evaluation using deep-learning-based acoustic signal processing. In Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 (pp. 961-964). (Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017; Vol. 2018-February). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2017.8282161

Swallowing function evaluation using deep-learning-based acoustic signal processing. / Kodama, Chisa; Kato, Kunihito; Tamura, Satoshi et al.
Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 961-964 (Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017; Vol. 2018-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kodama, C, Kato, K, Tamura, S & Hayamizu, S 2017, Swallowing function evaluation using deep-learning-based acoustic signal processing. in Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, vol. 2018-February, Institute of Electrical and Electronics Engineers Inc., pp. 961-964, 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, Kuala Lumpur, Malaysia, 17/12/12. https://doi.org/10.1109/APSIPA.2017.8282161

Kodama C, Kato K, Tamura S, Hayamizu S. Swallowing function evaluation using deep-learning-based acoustic signal processing. In Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 961-964. (Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017). doi: 10.1109/APSIPA.2017.8282161

Kodama, Chisa ; Kato, Kunihito ; Tamura, Satoshi et al. / Swallowing function evaluation using deep-learning-based acoustic signal processing. Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 961-964 (Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017).

@inproceedings{e27e0371c29d4a9c8227a65981ba5b03,

title = "Swallowing function evaluation using deep-learning-based acoustic signal processing",

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.",

author = "Chisa Kodama and Kunihito Kato and Satoshi Tamura and Satoru Hayamizu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/APSIPA.2017.8282161",

language = "English",

series = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "961--964",

booktitle = "Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017",

}

TY - GEN

T1 - Swallowing function evaluation using deep-learning-based acoustic signal processing

AU - Kodama, Chisa

AU - Kato, Kunihito

AU - Tamura, Satoshi

AU - Hayamizu, Satoru

PY - 2017/7/2

Y1 - 2017/7/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85050813498&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050813498&partnerID=8YFLogxK

U2 - 10.1109/APSIPA.2017.8282161

DO - 10.1109/APSIPA.2017.8282161

M3 - Conference contribution

AN - SCOPUS:85050813498

T3 - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017

SP - 961

EP - 964

BT - Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017

Y2 - 12 December 2017 through 15 December 2017

ER -

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

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this