  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.
