PDF(Pubmed)
Abstract：
Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases\' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.
摘要：
呼吸系统疾病是一个巨大的全球负担，需要有效的诊断方法进行及时干预。基于音频的数字生物标志物，声学,和上下呼吸系统发出的声音，以及声音，已成为呼吸功能的有价值指标。机器学习（ML）算法的最新进展为通过分析和处理此类基于音频的生物标志物来识别和诊断呼吸系统疾病提供了有希望的途径。越来越多的研究采用ML技术从音频生物标志物中提取有意义的信息。除了疾病识别，这些研究探索了不同的方面，例如在环境噪声中识别咳嗽声音，分析呼吸音，以检测呼吸道症状，如喘息和裂纹，以及语音/语音的分析，用于评估人类语音异常。为了提供更深入的分析，这篇综述审查了75项相关的音频分析研究，涉及三个不同的领域，涉及呼吸道疾病的症状：(a)咳嗽检测，(b)下呼吸道症状识别，和(c)来自语音和语音的诊断。此外，提供了该领域常用的公共可用数据集。据观察，研究趋势受到大流行的影响，随着对COVID-19诊断的研究激增，移动数据采集，和远程诊断系统。
