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Paper Abstract and Keywords
Presentation 2020-01-29 14:10
Proposal for a Method for Classifying Aortic Stenosis from ECG Using Deep Learning, and Analysis Using Grad-CAM.
Erika Hata, Chanjin Seo (Waseda Univ.), Masafumi Nakayama (Todachuo General Hospital), Kiyotaka Iwasaki (Waseda Univ.), Takaaki Ohkawauchi (Nihon Univ.), Jun Ohya (Waseda Univ.) MI2019-87
Abstract (in Japanese) (See Japanese page) 
(in English) Aortic valvular stenosis (AS), one of the valvular diseases, can cause sudden death in severe cases. This disease is rarely diagnosed only by ECG , and often by echocardiography. In this paper, we propose a method for classifying AS using deep learning whose input is only ECG. A one-beat image is generated from ECG data, and is classified into one of the two classes: "AS" or "not AS", by the deep learning. Here, a medical doctor annotates the images for training the deep learning network by looking at heart echocardiography. Using the trained network and Grad-CAM, features for "AS" or "not AS" are extracted as bounding boxes. As a result of experiments, the possibility of AS diagnosis using only ECG is shown, and the effectiveness of the feature extraction is obtained.
Keyword (in Japanese) (See Japanese page) 
(in English) ECG / AS / deep learning / model evaluation / Grad-CAM / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 399, MI2019-87, pp. 97-101, Jan. 2020.
Paper # MI2019-87 
Date of Issue 2020-01-22 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee MI  
Conference Date 2020-01-29 - 2020-01-30 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2020-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Proposal for a Method for Classifying Aortic Stenosis from ECG Using Deep Learning, and Analysis Using Grad-CAM. 
Sub Title (in English)  
Keyword(1) ECG  
Keyword(2) AS  
Keyword(3) deep learning  
Keyword(4) model evaluation  
Keyword(5) Grad-CAM  
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1st Author's Name Erika Hata  
1st Author's Affiliation Waseda University (Waseda Univ.)
2nd Author's Name Chanjin Seo  
2nd Author's Affiliation Waseda University (Waseda Univ.)
3rd Author's Name Masafumi Nakayama  
3rd Author's Affiliation Todachuo General Hospital (Todachuo General Hospital)
4th Author's Name Kiyotaka Iwasaki  
4th Author's Affiliation Waseda University (Waseda Univ.)
5th Author's Name Takaaki Ohkawauchi  
5th Author's Affiliation Nihon University (Nihon Univ.)
6th Author's Name Jun Ohya  
6th Author's Affiliation Waseda University (Waseda Univ.)
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Speaker Author-1 
Date Time 2020-01-29 14:10:00 
Presentation Time 10 minutes 
Registration for MI 
Paper # MI2019-87 
Volume (vol) vol.119 
Number (no) no.399 
Page pp.97-101 
#Pages
Date of Issue 2020-01-22 (MI) 


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