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Related Concept Videos

Pulse rhythm01:30

Pulse rhythm

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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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A pulse is a short burst of radio waves distributed over a range of frequencies that simultaneously excites all the nuclei in the sample. Upon passing a radio frequency pulse along the x-axis, the nuclei absorb energy corresponding to their Larmor frequencies and achieve resonance. This shifts the net magnetization vector from the z-axis toward the transverse plane. This angle of rotation of the magnetization vector, or the flip angle, is proportional to the duration and intensity of the pulse.
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A fast permutation entropy for pulse rate variability online analysis with one-sample recursion.

Jianan Zhan1, Zhengli Gan1, Lijuan Chou2

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

Medical Engineering & Physics
|October 14, 2023
PubMed
Summary
This summary is machine-generated.

A new Fast Permutation Entropy (FPE) algorithm significantly accelerates cardiovascular disease monitoring using pulse rate variability (PRV) signals on wearable devices. This innovation enables real-time analysis, improving arrhythmia detection accuracy to 85.43%.

Keywords:
Online processingPermutation entropyPortable wearable devicesPulsation signalPulse rate variability

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Cardiovascular Health

Background:

  • Pulse rate variability (PRV) signals are crucial for monitoring cardiovascular health, particularly in wearable devices.
  • The standard Permutation Entropy (PE) algorithm is effective for PRV analysis but computationally intensive for real-time applications.
  • Existing methods face challenges in processing PRV signals efficiently on resource-limited wearable devices.

Purpose of the Study:

  • To develop a computationally efficient algorithm for analyzing PRV signals suitable for online processing on wearable devices.
  • To introduce a Fast Permutation Entropy (FPE) algorithm that overcomes the limitations of traditional PE.
  • To validate the accuracy and performance of the FPE algorithm for cardiovascular monitoring and arrhythmia detection.

Main Methods:

  • A novel Fast Permutation Entropy (FPE) algorithm was developed, utilizing a microprocessor data updating process for single-sample recursive analysis.
  • The FPE algorithm was tested using simulation data and PRV signals from the "Fantasia database".
  • A low-cost wearable signal detection system was implemented to verify the online calculation capabilities of FPE.

Main Results:

  • The FPE algorithm demonstrated a significant speed improvement, being 211 times faster than PE for simulation data and 175 times faster for RRV signals.
  • FPE maintained high accuracy, with a Root Mean Squared Error of 0 for simulation data and processing times under 0.2 seconds for RRV signals.
  • Entropy-based features derived from FPE achieved an 85.43% classification accuracy in identifying life-threatening arrhythmias using decision trees.

Conclusions:

  • The proposed Fast Permutation Entropy (FPE) algorithm offers a computationally efficient and accurate method for analyzing PRV signals in real-time.
  • FPE is highly suitable for online cardiovascular disease monitoring applications on wearable devices.
  • The FPE algorithm shows significant potential for improving the detection of cardiovascular diseases, including arrhythmias.