Abstract

The photoplethysmogram (PPG) signal contains a wealth of information. However, there are several challenges to extracting physiological measurements from the PPG . Firstly, the PPG is highly susceptible to noise, which can corrupt the information in the signal. Consequently, much research has focused on assessing PPG signal quality and reducing motion artifact. Secondly, the PPG is often only indirectly affected by the physiology of interest, making it a challenge to extract physiological measurements such as blood pressure. This webinar will feature talks from researchers in the field of PPG signal processing, providing an overview of current work in the field and future research directions.

Speakers:

- Xiaorong Ding (University of Electronic Science and Technology of China) will present on Exploiting Photoplethysmogram Features for Cuffless Blood Pressure Estimation.

- Ariane Morassi-Sasso (Hasso Plattner Institute) will present on Predicting blood pressure from photoplethysmography.

- Serena Zanelli (Paris Sorbonne Nord University) will present a Deep learning approach to detect signal quality from clinical to non-clinical PPG devices.

Hosted by the Institute of Physics and Engineering in Medicine. Advanced registration required: https://www.ipem.ac.uk/what-s-on/ipem-events/webinar-photoplethysmography-signal-processing/