PPG Beat Reconstruction Based on Shape Models and Probabilistic Templates for Signals Acquired with Conventional Smartphones

Martín-Martínez, Diego; Domingues, Alexandre; Casaseca‐de‐la‐Higuera, Pablo; Alberola-López, Carlos; Sanches, J. Miguel

doi:10.1007/978-3-319-19390-8_67

Cited by 2 publications

(2 citation statements)

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“…Multiple attempts have been made to model the signal to disclose the underlying physiology [6,7,8,9,10,11,12,13,14]. Shariati and Zahedi [6] proposed a linear parametric model, and tested the fit with different choices for the correlation structure.…”

Section: Introductionmentioning

confidence: 99%

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

Regis¹,

Eerikäinen²,

Haakma³

et al. 2019

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%

“…The conclusion among the tested possibilities was in favour of the autoregressive structure. A more popular approach consists of decomposing the signal into a variable number of (log)normal components [7,8,9,10,11,12,13,14]. This method is motivated by the underlying physiology since each component has a physical explanation [15].…”

Section: Introductionmentioning

confidence: 99%