Two-Stage Motion Artifact Reduction Algorithm for rPPG Signals Obtained from Facial Video Recordings.

Recent years have witnessed the publication of many research articles regarding the contactless measurement and monitoring of heart rate signals deduced from facial video recordings. The techniques presented in these articles, such as examining the changes in the heart rate of an infant, provide a noninvasive assessment in many cases where the direct placement of any hardware equipment is undesirable. However, performing accurate measurements in cases that include noise motion artifacts still presents an obstacle to overcome. In this research article, a two-stage method for noise reduction in facial video recording is proposed. The first stage of the system consists of dividing each (30) seconds of the acquired signal into (60) partitions and then shifting each partition to the mean level before recombining them to form the estimated heart rate signal. The second stage utilizes the wavelet transform for denoising the signal obtained from the first stage. The denoised signal is compared to a reference signal acquired from a pulse oximeter, resulting in the mean bias error (0.13), root mean square error (3.41) and correlation coefficient (0.97). The proposed algorithm is applied to (33) individuals being subjected to a normal webcam for acquiring their video recording, which can easily be performed at homes, hospitals, or any other environment. Finally, it is worth noting that this noninvasive remote technique is useful for acquiring the heart signal while preserving social distancing, which is a desirable feature in the current period of COVID-19.

Signal Processing Method for Enhancement of Pulse Oximeter Signals

Pulse oximeters are now a part of every household first-aid kit, pulse oximeters have actually helped to primarily identify the severity of covid19 infection in a person's body. These devices measure the saturated blood oxygen level (SpO2) in a person's body, there by the displayed level of SpO2 helps medical professionals to hypothesize the situation and provide a better aid for the patient. Since the process is non-invasive, the devices are widely implemented. Pulse oximeters acquire photoplethysmographic (PPG) signals, these signals contain the volumetric changes in human blood, that on being exposed to mathematical principles give the SpO2 reading and other data. The process of obtaining the PPG signals through pulse oximetry employs a mechanism of emitting and detecting the IR and Red signals through human tissues, however during the capturing of reflected signals through detector, the detected signal comes along with noise referred as motion artifact (MA). These MAs arises due to the voluntary/involuntary movements of human causing volumetric changes in flow of blood at the source and detector sensor locations. The presence of MAs in such signals turns up to erroneous SpO2 level estimation, that creates a problem for medical professionals in treating the diseases. To improve the reliability of SpO2 estimation, by a pulse oximeter, the PPG signal quality is to be enhanced. In this paper, the authors tried to describe on the work of enhancing the acquired PPG signal quality by reducing MAs with effective methods. © 2022 IEEE.

On the use of Wavelet Transform based Adaptive Filtering for de-noising of Pulse Oximeter signals

Monitoring patient's blood oxygen saturation (SpO(2)) levels using pulse oximeter is important to physician. SpO(2) is also one of the major parameter that is being monitored to assess the respiratory health in Covid-19 infected patients during the ongoing pandemic. In pulse oximeters, the motion artifacts (MA), due to voluntary or involuntary movement of patient, will disturb the morphology of the photoplethysmographic (PPG) signals acquired through a finger/forehead sensor resulting in inaccurate SpO(2) values. The current work is focused on an efficient adaptive filtering method for MA reduction, which uses a wavelet reconstructed secondary MA noise as reference signal. It eliminates the use of an external sensor to be employed for estimating MA signal. This method while reducing the MA restored the PPG morphology and respiratory components facilitating accurate estimation SpO(2), heart rate (HR).

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.