Analysis on Four Derivative Waveforms of Photoplethysmogram (PPG) for Fiducial Point Detection

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Signal Processing

Background:

• Fiducial points in photoplethysmogram (PPG), VPG, and APG are crucial for cardiovascular disease diagnosis.
• Current methods for fiducial point detection often rely on complex algorithms.
• Inflection points in derivative waveforms offer potential for improved peak detection but are understudied.

Purpose of the Study:

• To investigate derivative waveforms of PPG, up to the fourth level.
• To utilize these derivatives to detect fiducial points of PPG, VPG, and APG.
• To establish reliable derivative markers for accurate peak detection.

Main Methods:

• Analysis of PPG waveforms from 43 ischemic heart disease (IHD) subjects.
• Observation of inflection points in derivative waveforms (up to fourth level).
• Application of moving average filters and thresholding for VPG and third derivative points.
• Validation using the CSL Pulse Oximetry Artifact Labels database.

Main Results:

• Identified reliable derivative markers for detecting PPG, VPG, and APG fiducial points.
• Achieved high accuracy in systolic peak detection (99.64% sensitivity, 99.38% positive predictivity) on the IHD dataset.
• Introduced the fourth derivative PPG waveform and its four key points for fiducial point detection.

Conclusions:

• Derivative waveforms, particularly the fourth derivative PPG, offer significant value for fiducial point detection.
• The proposed method provides a more robust and accurate approach to extracting diagnostic parameters from PPG signals.
• This technique can enhance the diagnosis of cardiovascular diseases by improving the reliability of PPG-derived metrics.