A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Jia Wei Chen, Hsin Kai Huang, Yu Ting Fang, Yen Ting Lin, Shih Zhang Li, Bo Wei Chen, Yu Chun Lo, Po Chuan Chen, Ching Fu Wang, You Yin Chen

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the pre-vention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environ-ment on BP. Out-of-office measurements are highly recommended by almost all hypertension or-ganizations. However, traditional ABPM devices based on the oscillometric technique usually in-terrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using mor-phological characteristics from PPG waveforms. As measurement can be affected by multiple vari-ables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter-and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grad-ing criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

Original languageEnglish
Article number1873
JournalSensors
Volume22
Issue number5
DOIs
Publication statusPublished - Mar 1 2022

Keywords

  • Blood pressure
  • Gaussian process regression
  • Machine learning
  • Photoplethysmography
  • Wearable devices

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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