A high performance biometric signal and image processing method to reveal blood perfusion towards 3D oxygen saturation mapping
journal contributionposted on 20.05.2016 by Ryan Imms, Sijung Hu, Vicente Azorin-Peris, Michael Trico, Ron Summers
Any type of content formally published in an academic journal, usually following a peer-review process.
Non-contact imaging photoplethysmography (PPG) is a recent development in the field of physiological data acquisition, currently undergoing a large amount of research to characterize and define the range of its capabilities. Contact-based PPG techniques have been broadly used in clinical scenarios for a number of years to obtain direct information about the degree of oxygen saturation for patients. With the advent of imaging techniques, there is strong potential to enable access to additional information such as multi-dimensional blood perfusion and saturation mapping. The further development of effective opto-physiological monitoring techniques is dependent upon novel modelling techniques coupled with improved sensor design and effective signal processing methodologies. The biometric signal and imaging processing platform (bSIPP) provides a comprehensive set of features for extraction and analysis of recorded iPPG data, enabling direct comparison with other biomedical diagnostic tools such as ECG and EEG. Additionally, utilizing information about the nature of tissue structure has enabled the generation of an engineering model describing the behaviour of light during its travel through the biological tissue. This enables the estimation of the relative oxygen saturation and blood perfusion in different layers of the tissue to be calculated, which has the potential to be a useful diagnostic tool.
The authors would like to thank the financial support of Loughborough University and the RCLEDs supplier WelTek Co. Ltd., Taiwan.
- Mechanical, Electrical and Manufacturing Engineering