Finger and ear photoplethysmogram waveform analysis by fitting with Gaussians

Area of Science:

• Cardiovascular Physiology
• Biomedical Engineering
• Signal Processing

Background:

• Blood volume pulse (VP) contour analysis offers insights into cardiovascular activity.
• Traditional methods rely on calculating derivatives of VP signals, which can be complex.
• Photoplethysmography (PPG) is a non-invasive technique to measure VP.

Purpose of the Study:

• To develop and validate a novel algorithm for analyzing VP waveforms from simultaneously measured ear and finger PPG signals.
• To accurately separate and model the systolic and diastolic waves of the VP using Gaussian functions.
• To compare the performance of the new algorithm with the traditional derivative method for VP analysis.

Main Methods:

• Simultaneously acquired ear and finger PPG signals from 40 healthy subjects.
• A novel algorithm that decomposes the VP into systolic and diastolic waves.
• Fitting each wave component with the sum of two Gaussian functions.
• Calculation of time values for direct and reflected waves, augmentation index (AI), and reflection index (RI).

Main Results:

• The novel algorithm successfully separated and modeled VP waveforms using Gaussian functions.
• Key cardiovascular parameters including direct and reflected wave timing, AI, and RI were accurately calculated.
• The new method demonstrated comparable results to the traditional derivative-based approach.
• Validation of the algorithm's efficacy across 40 healthy subjects.

Conclusions:

• The developed Gaussian function-based algorithm provides a robust and accurate method for VP waveform analysis.
• This novel approach offers an alternative to derivative-based methods, simplifying VP analysis.
• The algorithm's ability to extract cardiovascular parameters from PPG signals holds potential for clinical applications.