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Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
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Updated: Oct 22, 2025

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Real-Time Quality Index to Control Data Loss in Real-Life Cardiac Monitoring Applications.

Gaël Vila1,2, Christelle Godin1, Sylvie Charbonnier2

  • 1Univ. Grenoble Alpes, CEA, Leti, F-38000 Grenoble, France.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary
This summary is machine-generated.

A new quality index accurately estimates missing heart rate data from wearable sensors. This helps improve the reliability of heart rate variability (HRV) analysis in real-world applications.

Keywords:
electrocardiographyheart rate variabilityphotoplethysmographyreal-life measurementssignal qualitywearable cardiac sensors

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Area of Science:

  • Biomedical Engineering
  • Cardiovascular Monitoring
  • Wearable Technology

Background:

  • Wearable cardiac sensors are crucial for advanced heart rate variability (HRV) monitoring.
  • Photoplethysmography (PPG) devices often suffer from motion artifacts, leading to data loss in heart rate (HR) signals.
  • Accurate HR and HRV analysis is essential for reliable health monitoring.

Purpose of the Study:

  • Develop a "white-box" quality index to quantify missing samples in HR signals.
  • Assess the impact of data loss on HRV feature extraction from PPG signals.
  • Validate the performance of commercial PPG sensors against ECG benchmarks.

Main Methods:

  • Compared real-life recordings from Empatica E4 (PPG) and Zephyr BioHarness 3 (ECG) devices.
  • Developed and applied a quality index to estimate missing HR samples.
  • Validated PPG signal and feature extraction accuracy against ECG benchmarks.
  • Analyzed HR and HRV features at different levels of data loss.

Main Results:

  • The quality index accurately estimated mean HR (3.2% median error).
  • Short-term HRV features showed poor accuracy (64% median error for high-frequency power).
  • Longer-term HRV features had mild accuracy (25% median error for low-frequency power).
  • Using the quality index to select data windows with minimal loss significantly reduced errors.

Conclusions:

  • The proposed quality index is valuable for identifying reliable HR data from wearable sensors.
  • This tool can enhance the accuracy of HRV analysis in real-world settings.
  • The index facilitates more robust field validation studies for wearable cardiac monitoring devices.