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SDNN24 Estimation from Semi-Continuous HR Measures.

Davide Morelli1,2, Alessio Rossi3, Leonardo Bartoloni1

  • 1Huma Therapeutics Limited, London SW1P 4QP, UK.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

Wrist-worn devices can estimate cardiovascular health using heart rate (HR) data. This method approximates the standard deviation of the interval between QRS complexes (SDNN24), a key Heart Rate Variability (HRV) metric, by analyzing HR variations.

Keywords:
HRHRVLogistic RegressionSDNNcardiovascular riskneural network

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Area of Science:

  • Cardiology
  • Biomedical Engineering
  • Wearable Technology

Background:

  • The standard deviation of the interval between QRS complexes over 24 hours (SDNN24) is a crucial cardiovascular health indicator.
  • Wrist-worn fitness devices continuously monitor heartbeats, offering extensive user heart status data.
  • Traditional SDNN24 calculation relies on ECG, but motion artifacts and sensor differences in wearables hinder its estimation.

Purpose of the Study:

  • To develop an innovative method for estimating SDNN24 using only Heart Rate (HR) data from wearable devices.
  • To assess the feasibility of using HR data to approximate Heart Rate Variability (HRV) metrics for cardiovascular risk assessment.

Main Methods:

  • Calculated SDNN24 and Average ANN (SDANNHR24) derived from 24-hour HR data, using time windows of 1-60 minutes.
  • Employed Lomb-Scargle Periodogram for power spectrum analysis to assess frequency domain HRV parameters (ULF, VLF, LF, HF).
  • Compared HRV power spectra between subjects with low and high cardiovascular risk.

Main Results:

  • HR-derived SDNN24 (SDANNHR24) underestimates true SDNN24 due to missing high frequencies.
  • Distinct power spectrum differences were observed between low and high cardiovascular risk groups, particularly in ULF and VLF.
  • HR measures contain sufficient information to effectively discriminate cardiovascular risk.

Conclusions:

  • Semi-continuous HR monitoring from wrist-worn devices is adequate for estimating SDNN24 and assessing cardiovascular risk.
  • This approach offers a novel, accessible method for continuous cardiovascular health monitoring using readily available wearable data.