Improved Ultrasonic Local PWV Estimation Via Optimal Time Fiduciary Point Combination and Time Delays Fitness

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Medical Diagnostics

Background:

• Local pulse wave velocity (PWV) is crucial for assessing arteriosclerosis and diagnosing cerebrovascular diseases.
• Traditional ultrasonic transit time (TT) methods for PWV estimation are susceptible to errors from reflected waves and noise, impacting accuracy.
• Inaccurate time fiduciary point (TFP) positioning biases fitting performance and reduces the reliability of PWV measurements.

Purpose of the Study:

• To introduce a novel PGC (PSO-GA and Cook's distance) method for enhancing local PWV estimation using the ultrasonic transit time (TT) technique.
• To improve the accuracy and reduce variability in PWV measurements by optimizing multi-TFP (MTFP) combinations and time delay (TD) fitness.
• To provide more reliable diagnostic information for cerebrovascular diseases through enhanced PWV estimation.

Main Methods:

• Developed the PGC method, integrating Particle Swarm Optimization-Genetic Algorithm (PSO-GA) with Cook's distance for outlier detection.
• Employed an optimal multi-TFP (MTFP) combination strategy to calculate time delays (TDs).
• Utilized Cook's distance with a dynamic threshold and the reciprocal of the coefficient of determination as a fitness function for PSO-GA optimization of MTFP and threshold.

Main Results:

• The PGC method significantly reduced normalized root mean squared errors in simulations from 10.28 ± 2.51% to 5.96 ± 1.41%.
• In vivo experiments showed a decrease in the coefficient of variation for measured PWVs from 11.87% to 8.53%.
• Demonstrated improved accuracy and enhanced repeatability with reduced variability compared to traditional methods.

Conclusions:

• The proposed PGC method offers superior accuracy in PWV estimation compared to conventional approaches.
• The enhanced repeatability and reduced variability in vivo highlight the clinical potential of the PGC method for cerebrovascular disease diagnostics.
• This optimized TT-based PWV estimation provides a more robust tool for monitoring arteriosclerosis progression.