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 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) |
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MI2019-87 |
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) |
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Keyword(1) |
ECG |
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AS |
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deep learning |
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model evaluation |
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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 |
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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 |
5 |
Date of Issue |
2020-01-22 (MI) |
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