The Challenges in Neurosurgery during the COVID-19 pandemic: a systematic review (preprint)

Background The coronavirus disease-2019 (COVID-19) pandemic has created a global crisis unique to the health care system around the world. It also had a profound impact on the management of neurosurgical patients. In our research, we intended to investigate the effect of COVID-19 pandemic on neurosurgery, particular including vascular and oncological neurosurgery.Method Two investigators independently and systematically searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials (CENTRAL) to identify relevant studies respecting the criteria for inclusion and exclusion published up to June 30th, 2022. The outcomes of our research including mortality rate, length of stay, modified Rankin Score, delay in care, Glasgow outcome scale and major complications.Results Two investigators independently and systematically searched 1270 results from PubMed, Embase, Cochrane database, and extracted the detailed data from 13 articles assessed for eligibility, including 2 intracerebral hemorrhage, five subarachnoid hemorrhage, two neuro-oncology and 2 unspecified neurosurgery. A total of 25,864 patients were included in our research.Conclusion Some of our included studies suggested that pandemic caused negative effect on the outcomes of neurosurgery while others suggested that the pandemic didn't cause significant effect on the neurosurgery. Meanwhile, the effect of pandemic on neurosurgery may differ from different region.

Using a graph-based image segmentation algorithm for remote vital sign estimation and monitoring (preprint)

Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still an unmet need in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time-frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under no- and post-exercise conditions and repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements are observed in terms of precision, accuracy, and stability. We believe that the proposed estimation method will help address the need for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.