# Data Reduction Methodology for Dynamic Characteristic Extraction in Photoplethysmogram

## TL;DR

This paper explores how to reduce PPG data while maintaining accuracy in health monitoring, finding optimal sampling rates and time lengths for reliable results.

## Contribution

The study identifies optimal downsampling frequencies and time series lengths for reliable nonlinear analysis of PPG signals.

## Key findings

- A sampling frequency of 200 Hz balances accuracy and computational efficiency for PPG signals.
- Dynamical properties stabilize with a time series length of around 170 seconds, with less than 5% error.
- gPPG and rPPG show similar effectiveness in estimating dynamical properties under controlled conditions.

## Abstract

Photoplethysmogram (PPG) signals are increasingly utilized in wearable and mobile healthcare applications due to their non-invasive nature and ease of use in measuring physiological parameters, such as heart rate, blood pressure, and oxygen saturation. Recent advancements have highlighted green-light photoplethysmogram (gPPG) as offering superior signal quality and accuracy compared to traditional red-light photoplethysmogram (rPPG). Given the deterministic chaotic nature of PPG signals’ dynamics, nonlinear time series analysis has emerged as a powerful method for extracting health-related information not captured by conventional linear techniques. However, optimal data conditions, including appropriate sampling frequency and minimum required time series length for effective nonlinear analysis, remain insufficiently investigated. This study examines the impact of downsampling frequencies and reducing time series lengths on the accuracy of estimating dynamical characteristics from gPPG and rPPG signals. Results demonstrate that a sampling frequency of 200 Hz provides an optimal balance, maintaining robust correlations in dynamical indices while reducing computational load. Furthermore, analysis of varying time series lengths revealed that the dynamical properties stabilize sufficiently at around 170 s, achieving an error of less than 5%. A comparative analysis between gPPG and rPPG revealed no significant statistical differences, confirming their similar effectiveness in estimating dynamical properties under controlled conditions. These results enhance the reliability and applicability of PPG-based health monitoring technologies.