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Paper Abstract and Keywords
Presentation 2025-12-11 09:20
Automatic classification of neonatal bowel sounds using convolutional neural networks
Tatsuo Hosoba, Misa Fujita (Chukyo Univ.), Ryuichi Tanaka (Nagoya Univ. Hosp.) NLP2025-53
Abstract (in Japanese) (See Japanese page) 
(in English) In neonatal examinations, clinicians occasionally observe specific type of bowel sounds associated with vomiting. Althrough these sounds are typically assessed through abdominal auscultation, the evaluation relies heavily on the examiner’s experience and tends to be subjective. In this study, we propose an automatic classification method using a convolutional neural network (ConvNN) to improve the objectivity of bowel sound evaluation. Using bowel sound recordings collected from neonates, we evaluated the classification performance and found that the ConvNN achieved an accuracy of approximately 77% in identifying vomiting-related bowel sounds. These results suggest that the proposed method may support the detection of abnormal sounds during examination and contribute to more objective decision-making and rapid clinical assessment.
Keyword (in Japanese) (See Japanese page) 
(in English) neonatal bowel sounds / vomiting-related sounds / convolutional neural network / binary classification / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 283, NLP2025-53, pp. 6-9, Dec. 2025.
Paper # NLP2025-53 
Date of Issue 2025-12-04 (NLP) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NLP2025-53

Conference Information
Committee NLP  
Conference Date 2025-12-11 - 2025-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Kochi Castle Museum of History 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problem, etc 
Paper Information
Registration To NLP 
Conference Code 2025-12-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Automatic classification of neonatal bowel sounds using convolutional neural networks 
Sub Title (in English)  
Keyword(1) neonatal bowel sounds  
Keyword(2) vomiting-related sounds  
Keyword(3) convolutional neural network  
Keyword(4) binary classification  
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1st Author's Name Tatsuo Hosoba  
1st Author's Affiliation Chukyo University (Chukyo Univ.)
2nd Author's Name Misa Fujita  
2nd Author's Affiliation Chukyo University (Chukyo Univ.)
3rd Author's Name Ryuichi Tanaka  
3rd Author's Affiliation Nagoya University Hospital (Nagoya Univ. Hosp.)
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Speaker Author-1 
Date Time 2025-12-11 09:20:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # NLP2025-53 
Volume (vol) vol.125 
Number (no) no.283 
Page pp.6-9 
#Pages 4 
Date of Issue 2025-12-04 (NLP) 

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