# Robust probabilistic modeling of photoplethysmography signals with application to the classification of premature beats

## TL;DR

This paper introduces a robust Bayesian modeling approach for PPG signals that enhances understanding, classification, and monitoring of cardiac conditions, especially premature contractions, without relying on prior waveform assumptions.

## Contribution

It combines functional data analysis with state space models to robustly analyze PPG signals and enables classification of premature beats solely from model residuals.

## Key findings

- Effective detection of atrial and ventricular premature contractions.
- Model achieves high robustness and flexibility on stationary PPG signals.
- Facilitates data compression and medical parameter inference.

## Abstract

In this paper we propose a robust approach to model photoplethysmography (PPG) signals. After decomposing the signal into two components, we focus the analysis on the pulsatile part, related to cardiac information. The goal is to enable a deeper understanding of the information contained in the pulse shape, together with that derived from the rhythm. Our approach combines functional data analysis with a state space representation and guarantees fitting robustness and flexibility on stationary signals, without imposing a priori information on the waveform and heart rhythm. With a Bayesian approach, we learn the distribution of the parameters, used for understanding and monitoring PPG signals. The model can be used for data compression, for inferring medical parameters and to understand condition-related waveform characteristics. In particular, we detail a procedure for the detection of premature contractions based on the residuals of the fit. This method can handle both atrial and ventricular premature contractions, and classify the type by only using information from the model fit.