一种用于检测异常肺音的多分辨率分析方法。
A multiresolution analysis for detection of abnormal lung sounds.
作者信息
Emmanouilidou Dimitra, Patil Kailash, West James, Elhilali Mounya
机构信息
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:3139-42. doi: 10.1109/EMBC.2012.6346630.
Abstract
Automated analysis and detection of abnormal lung sound patterns has great potential for improving access to standardized diagnosis of pulmonary diseases, especially in low-resource settings. In the current study, we develop signal processing tools for analysis of paediatric auscultations recorded under non-ideal noisy conditions. The proposed model is based on a biomimetic multi-resolution analysis of the spectro-temporal modulation details in lung sounds. The methodology provides a detailed description of joint spectral and temporal variations in the signal and proves to be more robust than frequency-based techniques in distinguishing crackles and wheezes from normal breathing sounds.
摘要
自动分析和检测异常肺音模式对于改善肺部疾病标准化诊断的可及性具有巨大潜力，尤其是在资源匮乏的环境中。在当前研究中，我们开发了信号处理工具，用于分析在非理想噪声条件下记录的儿科听诊。所提出的模型基于对肺音频谱 - 时间调制细节的仿生多分辨率分析。该方法详细描述了信号中频谱和时间的联合变化，并且在区分正常呼吸音中的湿啰音和哮鸣音方面，被证明比基于频率的技术更稳健。
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