Assessment of arteriosclerosis based on lognormal fitting

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.