Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

Sen Yang, Yaping Zhang, Siu Yeung Cho, Ricardo Correia, Stephen P. Morgan

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)

Abstract

Conventional blood pressure (BP) measurement methods have different drawbacks such as being invasive, cuff-based or requiring manual operations. There is significant interest in the development of non-invasive, cuff-less and continual BP measurement based on physiological measurement. However, in these methods, extracting features from signals is challenging in the presence of noise or signal distortion. When using machine learning, errors in feature extraction result in errors in BP estimation, therefore, this study explores the use of raw signals as a direct input to a deep learning model. To enable comparison with the traditional machine learning models which use features from the photoplethysmogram and electrocardiogram, a hybrid deep learning model that utilises both raw signals and physical characteristics (age, height, weight and gender) is developed. This hybrid model performs best in terms of both diastolic BP (DBP) and systolic BP (SBP) with the mean absolute error being 3.23 ± 4.75 mmHg and 4.43 ± 6.09 mmHg respectively. DBP and SBP meet the Grade A and Grade B performance requirements of the British Hypertension Society respectively.

Original languageEnglish
Article number93
JournalOptical and Quantum Electronics
Volume53
Issue number2
DOIs
Publication statusPublished - 25 Jan 2021

Keywords

  • Blood pressure (BP)
  • Cuff-less
  • Deep learning
  • Electrocardiogram (ECG)
  • Photoplethysmogram (PPG)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model'. Together they form a unique fingerprint.

Cite this