Objectives: The aim of this study was to assess the efficacy of a visual noise feedback system and "quiet time" in reducing noise levels in the neonatal intensive care unit (NICU).
Design: A prospective cross-sectional study was performed in a combined level II/III NICU at a Canadian tertiary care hospital. Noise levels were recorded continuously for three weeks without and then three weeks with visual noise feedback system. Noise levels were compared after one year of using visual feedback, and subsequently with the addition of two "quiet times."
Results: Visual feedback reduced noise levels from 54.2 dB (95% CI 53.8-54.7 dB) to 49.4 dB (95% CI 48.9-49.8 dB; P < 0.0001) and increased the amount of time spent under 45 dB from 0 to 25% (P < 0.0001) after three weeks of use. However, this effect was not sustained at one year of visual feedback, with noise levels at 54.7 dB (95% CI 54.5-55.0 dB, P = 0.55). Quiet Time did not further reduce daily noise in the NICU (average noise levels 54.7, 95% CI 54.4-55.0 dB, P = 0.836).
Conclusions: While visual noise feedback system reduced noise levels in the short term, these effects were not sustainable at one year and could not be remediated with the addition of a Quiet Time initiative. Continuing education regarding the detrimental effects of noise is paramount to ensure persistent noise reduction in the NICU.
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http://dx.doi.org/10.1016/j.earlhumdev.2020.105073 | DOI Listing |
J Clin Med
December 2024
Department of Otorhinolaryngology, Head and Neck Surgery, Bhaarath Medical College, Chennai 600073, Tamil Nadu, India.
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School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
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January 2025
Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.
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Łukasiewicz Research Network-Tele and Radio Research Institute, Ratuszowa 11, 03-450 Warsaw, Poland.
The purpose of the experiment was to indicate which element of the production process of flexible printed circuit boards is optimal in terms of the reliability of final products. According to the Taguchi method, in the experiment, five factors with two levels each were chosen for the subsequent analysis. These included the number of conductive layers, the thickness of the laminate layer, the type of the laminate, the diameter of the plated holes, and the current density in the galvanic bath.
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December 2024
Department of Signal Processing and Multimedia Engineering, West Pomeranian University of Technology in Szczecin, al. Piastow 17, 70-310 Szczecin, Poland.
The safety of the airspace could be improved by the use of visual methods for the detection and tracking of aircraft. However, in the case of the small angular size of airplanes and the high noise level in the image, sufficient use of such methods might be difficult. By using the ConvNN (Convolutional Neural Network), it is possible to obtain a detector that performs the segmentation task for aircraft images that are very small and lost in the background noise.
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