ABSTRACT
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications. The emergence of novel coronavirus pneumonia COVID-19 has attracted worldwide attentions. Contact photoplethysmography (cPPG) methods need to contact the detection equipment with the patient, which may accelerate the spread of the epidemic. In the future, the non-contact heart rate detection will be an urgent need. However, existing heart rate measuring methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and wearing a mask). In this paper, we proposed a method of heart rate detection based on eye location of region of interest (ROI) to solve the problem of missing information when wearing masks. Besides, a model to filter outliers based on residual network was conceived first by us and the better heart rate measurement accuracy was generated. To validate our method, we also created a mask dataset. The results demonstrated that after using our method for correcting the heart rate (HR) value measured with the traditional method, the accuracy reaches 4.65 bpm, which is 0.42 bpm higher than that without correction.
ABSTRACT
We optimized a fluorescent quantitative polymerase chain reaction (qPCR) assay system for rapid and real time detection of SARS-CoV-2 RNA. The results show that the lowest dilution of RNA samples used for the detection of SARS-CoV-2 RNA could reach 1/10 000 (the initial value is set as 10 ng/µL). Moreover, the cycle threshold (Ct) for samples of clinically diagnosed COVID-19 was lower than 35 or 40. The sensitivity of this method was satisfactory. The results were consistent with those of the COVID-19 detection kit on the market under the same conditions, but the number of cycles required was shortened by about 2. Therefore, the optimized assay developed in this study can be used in screening and early clinical diagnosis. Our work provides a tool to facilitate rapid clinical diagnosis of COVID-19.