Purpose: To measure noise levels in the Saud Albabtain Cardiac Center cardiac surgical intensive-care unit (CSICU) at different locations to find out the prevalence of noise-induced hearing loss among female nurses.
Methods: Ambient CSICU noise was measured using a sound-level meter and personal noise dosimeter during morning and night shifts (12 hours each) for 30 days. An audiometry test and questionnaire were used to test nursing responses to noise levels.
Results: Mean 12-hour average noise levels at the station during night shift were 60.3±7.1 dB(A) and inside rooms 62.48±8.02 dB(A). However, during morning shift 64.1±8.4 dB(A) in the rooms was recorded, while 68.8±8.2 dB(A) was recorded at the station, with a significant difference between the shifts (<0.0001). ICU monitors recorded the highest noise-source levels of 82.7±5.3 dB(A). The lowest significant source was the suction machines, with an average of 67.1±12.5 dB(A). A significant correlation between decibel loss and nurse experience was observed.
Conclusion: Noise levels in the CSICU at Saud Albabtain Cardiac Center were higher than World Health Organization standards. CSICU nurses are exposed to noise levels that can affect their hearing capacity. Further research isneeded for effective medical device-alarm management.
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http://dx.doi.org/10.2147/JMDH.S222801 | DOI Listing |
J Clin Med
December 2024
Department of Otorhinolaryngology, Head and Neck Surgery, Bhaarath Medical College, Chennai 600073, Tamil Nadu, India.
The misuse of personalized listening devices (PLDs) resulting in noise-induced hearing loss (NIHL) has become a public health concern, especially among youths, including medical students. The occupational use of PLDs that produce high-intensity sounds amplifies the danger of cochlear deterioration and high-frequency NIHL especially when used in noisy environments. This study aims to evaluate the incidence and trends of NIHL among medical students using PLDs.
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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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Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai 519031, China.
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Łukasiewicz Research Network-Tele and Radio Research Institute, Ratuszowa 11, 03-450 Warsaw, Poland.
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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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