FlexiPulse : A machine-learning-enabled flexible pulse sensor for cardiovascular disease diagnostics

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

14 Scopus Citations
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Author(s)

  • Zhiqiang Ma
  • Changxin You
  • Xiao Yang
  • Shirong Qiu
  • Ni Zhao
  • Bryan P. Yan
  • Bee Luan Khoo

Detail(s)

Original languageEnglish
Article number101690
Journal / PublicationCell Reports Physical Science
Volume4
Issue number12
Online published20 Nov 2023
Publication statusPublished - 20 Dec 2023

Link(s)

Abstract

Recently, the flexible pulse sensor has emerged as a promising candidate for real-time and population-wide monitoring of cardiovascular health. However, most current technologies are prohibitively expensive, lack clinical validation, or are not designed to diagnose cardiovascular disease (CVD) events. Here, we present the development of FlexiPulse, a low-cost, clinically validated, intelligent, flexible pulse detection system for CVD monitoring and diagnostics. The porous graphene-based FlexiPulse is prepared by eco-friendly and economical laser direct-engraving techniques and is feasible for mass production. FlexiPulse achieves high accuracy (>93%), as confirmed by clinical techniques, enabling it to precisely detect subtle changes in cardiovascular status. Furthermore, incorporating machine-learning algorithms in FlexiPulse allows it to perform independent clinical assessments of actual CVD events, including atrial fibrillation and atrial septal defect, with an average accuracy of 98.7%. We believe that FlexiPulse has the potential to promote remote monitoring and in-home care, thereby advancing precision medicine and personalized healthcare significantly. © 2023 The Authors.

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