Objectives: To improve the infection control and prevention practices against coronavirus disease 2019 (COVID-19) in radiology department through loophole identification and providing rectifying measurements. Methods: Retrospective analysis of 2 cases of health-care-associated COVID-19 transmission in 2 radiology departments and comparing the infection control and prevention practices against COVID-19 with the practices of our department, where no COVID-19 transmission has occurred. Results: Several loopholes have been identified in the infection control and prevention practices against COVID-19 of the 2 radiology departments. Loopholes were in large part due to our limited understanding of the highly contagious coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is characterized by features not observed in other SARS viruses. We recommend to set up an isolation zone for handling patients who do not meet the diagnostic criteria of COVID-19 but are not completely cleared of the possibility of infection. Conclusions: Loopholes in the infection control and prevention practices against COVID-19 of the 2 radiology departments are due to poor understanding of the emerging disease which can be fixed by establishing an isolation zone for patients not completely cleared of SARS-CoV-2 infection.
Remote photoplethysmography (rPPG) is a noncontact technique for measuring cardiac signals from facial videos. High-quality rPPG pulse signals are urgently demanded in many fields, such as health monitoring and emotion recognition. However, most of the existing rPPG methods can only be used to get average heart rate (HR) values due to the limitation of inaccurate pulse signals. In this paper, a new framework based on generative adversarial network, called PulseGAN, is introduced to generate realistic rPPG pulse signals through denoising the chrominance signals.Considering that the cardiac signal is quasiperiodic and has apparent time-frequency characteristics, the error losses defined in time and spectrum domains are both employed with the adversarial loss to enforce the model generating accurate pulse waveforms as its reference. The proposed framework is tested on the public UBFC-RPPG database in both within-database and cross-database configurations. The results show that the PulseGAN framework can effectively improve the waveform quality, thereby enhancing the accuracy of HR, the heart rate variability (HRV) and the interbeat interval (IBI). The proposed method achieves the best performance compared to the denoising autoencoder (DAE) and CHROM, with the mean absolute error of AVNN (the average of all normalto-normal intervals) improving 20.85% and 41.19%, and the mean absolute error of SDNN (the standard deviation of all NN intervals) improving 20.28% and 37.53%, respectively, in the cross-database test. This framework can be easily extended to other existing deep learning-based rPPG methods, which is expected to expand the application scope of rPPG techniques.
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