Simulation framework for reflective PPG signal analysis depending on sensor placement and wavelength

Publication Type Conference Paper
Authors Maximilian Reiser, Andreas Breidenassel, Oliver Amft
Title Simulation framework for reflective PPG signal analysis depending on sensor placement and wavelength
Abstract We analyse the influence of reflective photoplethysmography (PPG) sensor positioning relative to blood vessels. A voxel based Monte Carlo simulation framework was developed and validated to simulate photon-tissue interactions. An anatomical model comprising a multi-layer skin description with a blood vessel is presented to simulate PPG sensor positioning at the volar wrist. The simulation framework was validated against standard test cases reported in literature. The blood vessel was considered in regular and dilated states. Simulations were performed with 10^8 photon packets and repeated five times for each condition, including wavelength, relative position of PPG sensor and vessel, and vessel dilation state. Statistical weights were associated to photon packets to represent absorption and scattering effects. A symmetrical arrangement of the PPG sensor around the blood vessel showed the maximum AC signal. When the PPG sensor was not centrally placed over the vessel, simulated photon weight in systolic and diastolic state deteriorated by ≥5% for both wavelengths. With a position-dependent variation of ≥5% at 660 nm and ≥12% at 940 nm of light absorption, blood had the most profound effect on signal quality. The mean penetration depth is dependent on the blood vessel position for both wavelengths. Our simulation results demonstrate the susceptibility of reflective PPG measurement to interference and could explain wearable PPG sensor performance variations related to positioning and wavelength.
Date 2022
Proceedings Title 2022 IEEE 18th International Conference on Wearable and Implantable Body Sensor Networks
Conference Name BSN 2022
Place Ioannina, Greece
Publisher IEEE
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Friedrich-Alexander-Universität Erlangen-Nürnberg