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Assessment of arteriosclerosis based on lognormal fitting.

Hao Tang1, Yumin Li1, Lulu Zhao1

  • 1State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People's Republic of China.

Physiological Measurement
|November 5, 2024
PubMed
Summary

This study introduces a novel method for assessing arteriosclerosis using improved pulse wave analysis and a lognormal function fit. The method accurately predicts cardiovascular health, aiding early detection in patients.

Keywords:
arteriosclerosislognormalpulse resolutionpulse wavesupport vector machine

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Area of Science:

  • Cardiovascular Physiology
  • Biomedical Engineering
  • Medical Diagnostics

Background:

  • Pulse pressure waves offer insights into human physiology and cardiovascular health.
  • Early detection and monitoring of arteriosclerosis are crucial for patient management.
  • Existing methods for assessing cardiovascular health require simpler, more accurate clinical tools.

Purpose of the Study:

  • To develop a simple and accurate method for assessing arteriosclerosis in clinical settings.
  • To enable convenient and effective early health monitoring for patients with arteriosclerosis.
  • To improve conventional electronic sphygmomanometers for enhanced pulse wave analysis.

Main Methods:

  • An arteriosclerosis assessment method was developed by fitting a lognormal function to pulse pressure waveforms.
  • An improved pulse resolution algorithm, combining waveform matching and threshold setting, was employed.
  • Pulse data acquired from 101 cases underwent preprocessing, including noise removal, baseline drift correction, and normalization.

Main Results:

  • Resolved lognormal function parameters showed significant correlation with brachial-ankle Pulse Wave Velocity (0.17-0.53 range).
  • These parameters serve as valuable reference indices for arteriosclerosis assessment.
  • A support vector machine-based arteriosclerosis assessment model achieved a prediction accuracy of 91.1%.

Conclusions:

  • The study presents a novel solution for arteriosclerosis assessment using advanced pulse wave analysis.
  • The developed pulse resolution algorithm offers significant improvements for multimodal pulse wave problems.
  • This approach holds substantial reference value for early detection and management of arteriosclerosis.