Arterial Blood Pressure Estimation Method from Electrocardiogram Signals using U-Net

Rikuto Yoshizawa, Kohei Yamamoto, Tomoaki Ohtsuki

研究成果: Conference contribution

抄録

Previous works proposed deep learning models to estimate blood pressure from electrocardiogram (ECG) signals. However, they can only estimate max, min, and mean arterial blood pressures and cannot estimate arterial blood pressure (ABP). This paper presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with signals of about 185 hours, we observed that the proposed method estimated ABP with high accuracy. Furthermore, the accuracies of the calculated max\min and mean ABPs were comparable to those in the previous works, even though our method also estimated ABP. In the end, we discussed the subject-overfitting problem and future work toward practical use of daily blood pressure monitoring.

本文言語English
ホスト出版物のタイトル44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2689-2692
ページ数4
ISBN(電子版)9781728127828
DOI
出版ステータスPublished - 2022
イベント44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
継続期間: 2022 7月 112022 7月 15

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
2022-July
ISSN(印刷版)1557-170X

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
国/地域United Kingdom
CityGlasgow
Period22/7/1122/7/15

ASJC Scopus subject areas

  • 信号処理
  • 生体医工学
  • コンピュータ ビジョンおよびパターン認識
  • 健康情報学

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