Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal

Safa Cherif 1, 2
2 Lab-STICC_IMTA_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : One promising area of research in clinical routine involves using photoplethysmography (PPG) for monitoring respiratory activities. PPG is an optical signal acquired from oximeters, whose principal use consists in measuring oxygen saturation. Despite its simplicity of use, the deployment of this technique is still limited because of the signal sensitivity to distortions and the non-reproducibility between subjects, but also for the same subject, due to age and health conditions. The main aim of this work is to develop robust and universal methods for estimating accurate respiratory rate regardless of the intra- and inter-individual variability that affects PPG features. For this purpose, firstly, an adaptive artefact detection method based on template matching and decision by Random Distortion Testing is introduced for detecting PPG pulses with artefacts. Secondly, an analysis of several spectral methods for Respiratory Rate (RR) estimation on two different databases, with different age ranges and different respiratory modes, is proposed. Thirdly, a Spectral Respiratory Quality Index (SRQI) is attributed to respiratory rate estimates, in order that the clinician may select only RR values with a large confidence scale. Promising results are found for two different databases.
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Safa Cherif. Effective signal processing methods for robust respiratory rate estimation from photoplethysmography signal. Signal and Image processing. Ecole nationale supérieure Mines-Télécom Atlantique, 2018. English. ⟨NNT : 2018IMTA0094⟩. ⟨tel-02160161⟩

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