Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study

Kun chan Lan, Paweeya Raknim, Wei Fong Kao, Jyh How Huang

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

Heart rate variability (HRV) is often used to assess the risk of cardiovascular disease, and data on this can be obtained via electrocardiography (ECG). However, collecting heart rate data via photoplethysmography (PPG) is now a lot easier. We investigate the feasibility of using the PPG-based heart rate to estimate HRV and predict diseases. We obtain three months of PPG-based heart rate data from subjects with and without hypertension, and calculate the HRV based on various forms of time and frequency domain analysis. We then apply a data mining technique to this estimated HRV data, to see if it is possible to correctly identify patients with hypertension. We use six HRV parameters to predict hypertension, and find SDNN has the best predictive power. We show that early disease prediction is possible through collecting one’s PPG-based heart rate information.

Original languageEnglish
Article number103
JournalJournal of Medical Systems
Volume42
Issue number6
DOIs
Publication statusPublished - Jun 1 2018

Keywords

  • E-health
  • Heart rate monitoring
  • Home health monitoring
  • Sensor technology
  • Telehealth
  • Wireless sensor networks

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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