Awwab Qasim Jumaah Althahab, Branislav Vuksanovic, Mohamed Al-Mosawi, Hongjie Ma
{"title":"Assessing the Acoustic Noise in Intensive Care Units via Deep Learning Technique","authors":"Awwab Qasim Jumaah Althahab, Branislav Vuksanovic, Mohamed Al-Mosawi, Hongjie Ma","doi":"10.1007/s40857-024-00321-3","DOIUrl":null,"url":null,"abstract":"<div><p>Intensive care unit (ICU) noise is a critical and often overlooked issue, impacting patient recovery and healthcare staff well-being. Existing research primarily relies on costly sound level meters for monitoring noise levels, where the characteristics of noise sources cannot be determined and discriminated. This study employs deep neural networks to detect and classify ICU noise events, enhancing source identification. A cost-effective internet of things-based audio recording and monitoring system has been designed and deployed in three ICUs for data collection. The acoustic event classification system described in the paper integrates convolutional neural networks for event detection, followed by clustering to isolate noise sources. Results demonstrate precise classification, with speech identified as a major contributor in all ICUs. This model offers valuable insights for characterising acoustic sources in typical ICUs, which could be the first step towards tackling the problem of excessive noise in ICUs as well as a starting point for further research in this area.</p></div>","PeriodicalId":54355,"journal":{"name":"Acoustics Australia","volume":"52 2","pages":"209 - 224"},"PeriodicalIF":1.7000,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acoustics Australia","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.1007/s40857-024-00321-3","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Intensive care unit (ICU) noise is a critical and often overlooked issue, impacting patient recovery and healthcare staff well-being. Existing research primarily relies on costly sound level meters for monitoring noise levels, where the characteristics of noise sources cannot be determined and discriminated. This study employs deep neural networks to detect and classify ICU noise events, enhancing source identification. A cost-effective internet of things-based audio recording and monitoring system has been designed and deployed in three ICUs for data collection. The acoustic event classification system described in the paper integrates convolutional neural networks for event detection, followed by clustering to isolate noise sources. Results demonstrate precise classification, with speech identified as a major contributor in all ICUs. This model offers valuable insights for characterising acoustic sources in typical ICUs, which could be the first step towards tackling the problem of excessive noise in ICUs as well as a starting point for further research in this area.
期刊介绍:
Acoustics Australia, the journal of the Australian Acoustical Society, has been publishing high quality research and technical papers in all areas of acoustics since commencement in 1972. The target audience for the journal includes both researchers and practitioners. It aims to publish papers and technical notes that are relevant to current acoustics and of interest to members of the Society. These include but are not limited to: Architectural and Building Acoustics, Environmental Noise, Underwater Acoustics, Engineering Noise and Vibration Control, Occupational Noise Management, Hearing, Musical Acoustics.