pyPPG: a python toolbox for PPG morphological analysis.


License
AGPL-3.0
Install
pip install pyPPG==1.0.71

Documentation

pyPPG toolbox documentation

Photoplethysmogram (PPG) beat detection, fiducial points detection, and comprhessive assessment of standard biomarkers.

Introduction

The pyPPG is a standardised toolbox to analyze long-term finger PPG recordings in real-time. The state-of-the-art PPG biomarkers have been identified, implemented in the pyPPG toolbox, and validated on standard, public available PPG databases. The pyPPG using the improved Automatic Beat Detector (Aboy et al. 2005). The PPG peak and onset detection are implemented with. The pyPPG offers a robust beat detection, and a comprhessive assessment of clinically relevant biomarkers of continuous PPG time series.

Description

The following steps are implemented in the pyPPG toolbox:

  1. Loading a raw PPG signal: The toolbox can accept various file formats such as .mat, .csv, .txt, or .edf. These files should contain raw PPG data along with the corresponding sampling rate.
  2. Preprocessing: The raw signal is filtered to remove unwanted noise and artifacts. Subsequently, the signal is resampled to a uniform rate of 75 Hz.
  3. Pulse wave segmentation: The toolbox employs a peak detector to identify the systolic peaks. Based on the peak locations, the toolbox also detects the pulse onsets and offsets, which indicate the start and end of the PPG pulse waves.
  4. Fiducial points identification: For each pulse wave, the toolbox detects a set of fiducial points.
  5. Biomarker engineering: Based on the fiducial points, a set of 74 PPG digital biomarkers are engineered.

The pyPPG toolbox also provides an optional PPG signal quality index based on the Matlab implementation of the work by (Li et al. 2015).

img.png.

The toolbox identifies individual pulse waves in a PPG signal by identifying systolic peaks (sp), and then identifying the pulse onset (on) and offset (off) on either side of each systolic peak which indicate the start and end of the pulse wave, respectively.

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Installation

Available on pip, with the command: pip install pyPPG

Requirements

Python Requirements:

Python >= 3.10

scipy == 1.9.1

numpy == 1.23.2

dotmap == 1.3.30

pandas == 1.5.0

wfdb == 4.0.0

mne == 1.2.0

All the python requirements are installed when the toolbox is installed, no need for additional commands.

Documentation:

https://pyppg.readthedocs.io/en/latest/

The main components:

  1. Software
    • An open-source algorithmic pyPPG toolbox, which implements the PPG peak and onsets detection algorithms and prefiltering routines. This can be used within your own data analysis code using the pyPPG API.
  2. Databases
    • The PPG signals are based on the PhysioNet Databases. Available here .
  3. Configuration
    • A set of configuration files that adapt the PPG peak detection and pyPPG algorithms to work with sleep PPG data.

All PPG measures can be further adapted for the analysis for efficient heart rate measurement as well as health assessment with clinically relevant biomarkers.