An Autocorrelation Modeling Method for Oxygen Saturation Measurement During Low Perfusion

Background: SpO2 is a widely used estimation of oxygen saturation owing to its convenient usage and low cost. However, SpO2 determination under low perfusion condition is severely affected by noise.Methods: In this paper, an autocorrelation modeling method for the oxygen saturation measurement during low perfusion is presented. The proposed method mainly contains two steps: calculating the autocorrelation of the photoplethysmography (PPG) signals and modeling for the parameter calculation. The autocorrelation of the PPG signals can suppress the noise and extract pulse waves from low perfusion signals. The model can realize the calculation of SpO2 with the autocorrelation signals. Results: Experiments showed that the new method had a good accuracy and stability under low perfusion condition (perfusion index (PI)<=0.2%), and it was also motion-tolerant. Meanwhile, the new method showed a good performance for the oxygen saturation measurement under the condition of lower perfusion(PI=0.1%).Conclusions: The new method could realize the calculation of SpO2 by little computation and high efficiency without extra hardware. It has strong potential in real-time clinical use.

Detection of lung hypoperfusion in Covid-19 patients during recovery by digital imaging quantification

SummaryPurposeA large number of patients affected by the SARS-Cov-2 virus worldwide undergo recovery of symptoms in about one month. Among these patients, the healing process is still under observation, with some patients in need of careful clinical monitoring. While the radiological findings have been shown to regress almost completely, little knowledge is available at the moment about other complications in the lung and in other organs. We then investigated the lung perfusion conditions in patients affected by COVID-19 during recovery.MethodWe retrospectively studied 20 patients, from 14 to 60 days after resolution of the COVID-19 symptoms, using chest CT. In a subgroup of 5 patients contrasted CT was used. Beside normal radiological evaluation of lung tissue, perfusion conditions were evaluated by digital image processing in the lung volume automatically segmented.ResultsPulmonary lung evaluation showed that COVID-19 pneumonia almost completely regressed, with mild focal areas affected by fibrous stripes. In patients that reported dyspnea, lung CT showed complete resolution of interstitial changes. Quantification of lung perfusion condition by contrasted CT, showed that dyspnea in 3 patients was associated with areas of hypoperfusion, while in 2 patients not reporting dyspnea perfusion conditions were comparable to normal controls.ConclusionsAlthough we obtained preliminary data, this is the first report on quantitative evaluation of hypoperfused lung tissue detected in recovering COVID-19 patients. These results suggest the need to further investigate these patients and to redefine the role of CT evaluation for diagnostic purposes as well as for evaluation of potential treatments.FundingThis was an academic study that received no direct funding.