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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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When the heart pumps blood out, arterial elastic fibers play a crucial role in sustaining a high-pressure gradient. They expand to accommodate the received blood and then recoil - a process known as the pulse that can be either manually palpated or electronically quantified. Despite a reduction in its effect with increased distance from the heart, elements of the pulse's systolic and diastolic components persist, observable even at the arteriole level.
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A fast sample entropy for pulse rate variability analysis.

Lijuan Chou1,2, Shengrong Gong2,3, Haiping Yang1

  • 1School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou, 215500, Jiangsu, China.

Medical & Biological Engineering & Computing
|February 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a faster sample entropy method for analyzing pulse rate variability (PRV) signals. The improved method significantly speeds up calculations without compromising accuracy, enabling real-time physiological signal processing.

Keywords:
Low time-consumptionPulse rate variabilityPulse signalSample entropySignal processing in real time

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Area of Science:

  • Biomedical Engineering
  • Nonlinear Dynamics
  • Signal Processing

Background:

  • Sample entropy is a key nonlinear index for analyzing pulse rate variability (PRV).
  • Traditional sample entropy calculation is computationally intensive and time-consuming.
  • Efficient analysis of PRV signals is crucial for real-time physiological monitoring.

Purpose of the Study:

  • To develop a fast sample entropy calculation method for PRV signals.
  • To address the computational burden and time consumption of standard sample entropy.
  • To enhance the feasibility of real-time analysis of physiological data.

Main Methods:

  • A novel fast sample entropy algorithm was developed based on microprocessor data updating principles.
  • The method was validated using both simulated datasets and real-world PRV signals.
  • Accuracy and computational efficiency were compared against the standard sample entropy method.

Main Results:

  • The improved sample entropy method achieved significant speed enhancements, up to 47.6 times faster on simulated data (average 28.6 times).
  • The entropy values calculated by the fast method remained consistent with the original sample entropy.
  • On PRV signals, the method demonstrated a speed increase of approximately 35 times.

Conclusions:

  • The proposed fast sample entropy method offers a substantial improvement in computational efficiency for PRV signal analysis.
  • This advancement holds significant potential for real-time processing of physiological signals.
  • The method provides a viable solution for overcoming the limitations of traditional sample entropy in time-sensitive applications.