Acoustic environment is an important part of the overall environment of a hospital. Acoustic environmental pollution will have varying degrees of impact on human physiology and psychology. Acoustic environmental pollution in outpatient clinics has become a major concern for visitors and medical staff. Exploring the causes of outpatient acoustic environment pollution and adopting active countermeasures are effective methods to control outpatient acoustic environment pollution. This article will review the current situation of acoustic environmental pollution in outpatient clinics and the impact of acoustic environmental pollution on medical staff and visitors, and analyze the common causes of outpatient acoustic environmental pollution based on actual conditions, and propose corresponding solutions for the corresponding causes. It aims to provide a reference for clinically effective control of acoustic environmental pollution in outpatient clinics.
The portable light-weight magnetic resonance imaging system can be deployed in special occasions such as Intensive Care Unit (ICU) and ambulances, making it possible to implement bedside monitoring imaging systems, mobile stroke units and magnetic resonance platforms in remote areas. Compared with medium and high field imaging systems, ultra-low-field magnetic resonance imaging equipment utilizes light-weight permanent magnets, which are compact and easy to move. However, the image quality is highly susceptible to external electromagnetic interference without a shielded room and there are still many key technical problems in hardware design to be solved. In this paper, the system hardware design and environmental electromagnetic interference elimination algorithm were studied. Consequently, some research results were obtained and a prototype of portable shielding-free 50 mT magnetic resonance imaging system was built. The light-weight magnet and its uniformity, coil system and noise elimination algorithm and human brain imaging were verified. Finally, high-quality images of the healthy human brain were obtained. The results of this study would provide reference for the development and application of ultra-low-field magnetic resonance imaging technology.
The performance of a pulse oximeter based on photoelectric detection is greatly affected by motion noise (MA) in the photoplethysmographic (PPG) signal. This paper presents an algorithm for detecting motion oxygen saturation, which reconstructs a motion noise reference signal using ensemble of complete adaptive noise and empirical mode decomposition combined with multi-scale permutation entropy, and eliminates MA in the PPG signal using a convex combination least mean square adaptive filters to calculate dynamic oxygen saturation. The test results show that, under simulated walking and jogging conditions, the mean absolute error (MAE) of oxygen saturation estimated by the proposed algorithm and the reference oxygen saturation are 0.05 and 0.07, respectively, with means absolute percentage error (MAPE) of 0.05% and 0.07%, respectively. The overall Pearson correlation coefficient reaches 0.971 2. The proposed scheme effectively reduces motion artifacts in the corrupted PPG signal and is expected to be applied in portable photoelectric pulse oximeters to improve the accuracy of dynamic oxygen saturation measurement.