A movement-artefact-free heart-rate prediction system

Maarten Thoonen, Peter H. Veltink, F.R. Halfwerk, Robby van Delden, Ying Wang*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

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Abstract

Continuous automatic heart rate (HR) monitoring plays a crucial role in timely intervention for postoperative patients. However, for effective alarm management, patients’ activities of daily living need to be considered as they influence HR. This explorative study aimed to develop a heartrate prediction system while performing six activities. An experiment with fourteen participants was conducted to gather data to build a system. This system consisted of a support-vector machine classifier for activity recognition and a k-Nearest Neighbors regressor for HR prediction.
The R-squared (a goodness-of-fit measure) of the HR predictor is 79% on average. Given the heterogeneity of different populations, the system will be further tested and developed using patient datasets in future towards linical practice applications.
Original languageEnglish
Number of pages4
DOIs
Publication statusE-pub ahead of print/First online - 31 Aug 2022
Event49th Computing in Cardiology Conference, CinC 2022 - Tampere, Finland
Duration: 4 Sep 20227 Sep 2022
Conference number: 49
https://cinc.org/

Conference

Conference49th Computing in Cardiology Conference, CinC 2022
Abbreviated titleCinC 2022
Country/TerritoryFinland
CityTampere
Period4/09/227/09/22
Internet address

Keywords

  • Vital signs
  • heart rate monitoring
  • Human Activity Recognition
  • heart rate prediction
  • Accelerometer

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