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Multi-Gaussian fitting for pulse waveform using Weighted Least Squares and multi-criteria decision making method.

Lu Wang¹, Lisheng Xu, Shuting Feng

¹College of Information Science and Engineering, Northeastern University, Shenyang City, Liaoning Province, 110819, China.

Computers in Biology and Medicine

|November 12, 2013

Summary

This study introduces a Multi-Gaussian (MG) model for analyzing cardiovascular pulse waveforms. The novel method accurately estimates key waveform points, improving cardiovascular condition assessment.

Keywords:

Multi-Gaussian model Multi-criteria decision making Photoplethysmography Pulse decomposition analysis Pulse waveform

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Published on: September 1, 2014

Area of Science:

Biomedical Engineering
Cardiovascular Physiology
Signal Processing

Background:

Pulse waveform analysis offers non-invasive cardiovascular insights.
Existing Pulse Decomposition Analysis (PDA) methods have limitations in accurately estimating key waveform points.
Accurate estimation of key pulse wave points is crucial for assessing vascular conditions.

Purpose of the Study:

To propose a novel Multi-Gaussian (MG) model for fitting and analyzing pulse waveforms.
To enhance the accuracy of key point estimation in pulse waveform analysis.
To develop a more effective method for cardiovascular condition assessment through pulse waveform analysis.

Main Methods:

Utilized a Multi-Gaussian (MG) model with an adaptive number of Gaussian waves (4 or 5).
Employed the Weighted Least Squares (WLS) method for estimating model parameters.
Applied Multi-Criteria Decision Making (MCDM) for optimizing weight values in the WLS method.
Validated the model by fitting 150 real pulse waveforms across five types.

Main Results:

Achieved a Normalized Root Mean Square Error (NRMSE) of less than 2.0%.
Demonstrated satisfactory estimation accuracy for key pulse waveform points.
The MG model effectively compressed, synthesized, and analyzed pulse waveforms.

Conclusions:

The proposed Multi-Gaussian (MG) model with WLS and MCDM provides an effective approach for pulse waveform analysis.
The method significantly improves the accuracy of key point estimation compared to existing PDA techniques.
This technique holds promise for enhanced non-invasive cardiovascular diagnostics.