Classification of Aortic Stenosis Using ECG by Deep Learning and its Analysis Using Grad-CAM

Erika Hata, Chanjin Seo, Masafumi Nakayama, Kiyotaka Iwasaki, Takaaki Ohkawauchi, Jun Ohya

研究成果: Conference contribution

抄録

This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using ECG (Electrocardiogram) images by the deep learning whose training ECG images are annotated by the diagnoses given by the medical doctor who observes the echocardiograms. Besides, it explores the relationship between the trained deep learning network and its determinations, using the Grad-CAM.In this study, one-beat ECG images for 12-leads and 4-leads are generated from ECG's and train CNN's (Convolutional neural network). By applying the Grad-CAM to the trained CNN's, feature areas are detected in the early time range of the one-beat ECG image. Also, by limiting the time range of the ECG image to that of the feature area, the CNN for the 4-lead achieves the best classification performance, which is close to expert medical doctors' diagnoses.Clinical Relevance - This paper achieves as high AS classification performance as medical doctors' diagnoses based on echocardiograms by proposing an automatic method for detecting AS only using ECG.

本文言語English
ホスト出版物のタイトル42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
ホスト出版物のサブタイトルEnabling Innovative Technologies for Global Healthcare, EMBC 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1548-1551
ページ数4
ISBN(電子版)9781728119908
DOI
出版ステータスPublished - 2020 7
イベント42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 - Montreal, Canada
継続期間: 2020 7 202020 7 24

出版物シリーズ

名前Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
2020-July
ISSN(印刷版)1557-170X

Conference

Conference42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
CountryCanada
CityMontreal
Period20/7/2020/7/24

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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