Objective. After skin flap transplants, perfusion strength monitoring is essential for the early detection of tissue perfusion disorders and thus to ensure the survival of skin flaps. Camera-based photoplethysmography (cbPPG) is a non-contact measurement method, using video cameras and ambient light, which provides spatially resolved information about tissue perfusion. It has not been researched yet whether the measurement depth of cbPPG, which is limited by the penetration depth of ambient light, is sufficient to reach pulsatile vessels and thus to measure the perfusion strength in regions that are relevant for skin flap transplants. Approach. We applied constant negative pressure (compared to ambient pressure) to the anterior thighs of 40 healthy subjects. Seven measurements (two before and five up to 90 min after the intervention) were acquired using an RGB video camera and photospectrometry simultaneously. We investigated the performance of different algorithmic approaches for perfusion strength assessment, including the signal-to-noise ratio (SNR), its logarithmic components logS and logN, amplitude maps, and the amplitude height of alternating and direct signal components. Main results. We found strong correlations of up to r = 0.694 (p < 0.001) between photospectrometric measurements and all cbPPG parameters except SNR when using the green color channel. The transfer of cbPPG signals to POS, CHROM, and O3C did not lead to systematic improvements. However, for direct signal components, the transformation to O3C led to correlations of up to r = 0.744 (p < 0.001) with photospectrometric measurements. Significance. Our results indicate that a camera-based perfusion strength assessment in tissue with deep-seated pulsatile vessels is possible.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:80505 |
| Date | 26 August 2022 |
| Creators | Hammer, Alexander, Scherpf, Matthieu, Schmidt, Martin, Ernst, Hannes, Malberg, Hagen, Matschke, Klaus, Dragu, Adrian, Martin, Judy, Bota, Olimpiu |
| Publisher | IOP Publishing |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 0967-3334, 025007, 10.1088/1361-6579/ac557d, info:eu-repo/grantAgreement/European Commission/Horizon2020/101017424//A patient-centered early risk prediction, prevention, and intervention platform to support the continuum of care in coronary artery disease (CAD) using eHealth and artificial intelligence/TIMELY |
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