Welcome to pyPSG documentation!

Introduction

Dedicated toolboxes have been developed for the analysis of individual physiological signals. However, for the purpose of fast and efficient data analysis, there was a need to unify these separate modules. To address this gap, pyPSG was developed — a Python-based toolbox capable of handling and analyzing various types of physiological signals within a single environment. The toolbox standardizes the signal processing workflow across data from different sources and also enables the preprocessing of physiological signals for machine learning applications. During development, modularity was a key consideration to allow future expansion with additional biological signals.

Description

Input data, raw signals are first unified in the EDF (European Data Format) structure. The pipeline processes three parallel signal branches: PPG, ECG, and SpO2. Each signal undergoes preprocessing using its corresponding module (pyPPG, pecg, or pobm). In the case of PPG and ECG, fiducial points are detected to derive beat-to-beat intervals, which are then forwarded to the mhrv module for HRV/BRV analysis. Biomarkers are extracted separately from all three signal types, and the results are aggregated and saved for further data analysis.