Homecare-Oriented Intelligent Long-Term Monitoring of Blood Pressure Using Electrocardiogram Signals

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

29 Scopus Citations
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Author(s)

  • Xiaomao Fan
  • Hailiang Wang
  • Fan Xu
  • Yang Zhao
  • Kwok-Leung Tsui

Detail(s)

Original languageEnglish
Article number8943989
Pages (from-to)7150-7158
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume16
Issue number11
Online published27 Dec 2019
Publication statusPublished - Nov 2020

Abstract

Long-term blood pressure (BP) monitoring is a widely used approach in a homecare intelligent system. However, BP is usually measured using cuff-based devices with tedious operation in practice, which may not be cost effective for continuous BP tracking. In this article, we propose a novel attention-based multitask network with a weighting scheme for BP estimation by analyzing and modeling single lead electrocardiogram (ECG) signals. Experimental results demonstrate that the proposed method could achieve mean error of systolic blood pressure, diastolic blood pressure, and mean arterial pressure estimation in levels of 0.18 ± 10.83, 1.24 ± 5.90, and 0.84 ± 6.47 mmHg, respectively. In comparison to other cutting-edge methods using ECG signals, the proposed method shows superior BP estimation performance. By integrating with a wearable/portable ECG monitoring device, the proposed model can be deployed to an embedded system or remote healthcare intelligent system to provide long-term BP monitoring service, which would help to reduce the incidence of malignant events happened in hypertensive population.

Research Area(s)

  • Attention mechanism, blood pressure, deep learning, electrocardiogram signals, healthcare monitoring, multiple tasks

Citation Format(s)

Homecare-Oriented Intelligent Long-Term Monitoring of Blood Pressure Using Electrocardiogram Signals. / Fan, Xiaomao; Wang, Hailiang; Xu, Fan et al.
In: IEEE Transactions on Industrial Informatics, Vol. 16, No. 11, 8943989, 11.2020, p. 7150-7158.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review