An Automated Computerized Auscultation and Diagnostic System for Pulmonary Diseases
An Automated Computerized Auscultation and Diagnostic System for Pulmonary Diseases
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DOI：
10.1007/s10916-009-9334-1
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发表时间：
2010-12-01
影响因子：
5.3
通讯作者：
Fahim, Atef
中科院分区：
文献类型：
--
作者：
Abbas, Ali;Fahim, Atef
呼吸音非常重要，因为它们提供了有关呼吸系统健康的宝贵信息。从呼吸系统发出的声音是不均匀的，并且随着时间的推移，从一个人到另一个人以及对于同一个人变化很大。它们本身并不是疾病的直接证据，而是疾病存在的推论。听诊诊断是一种通过实践获得和磨练的艺术/技能;因此，通常使用侵入性和潜在有害的成像诊断技术（如X射线）进行确认。本研究的重点是开发一个自动听诊诊断系统，克服了传统听诊技术固有的局限性。该系统使用前端声音信号滤波模块，该模块使用自适应神经网络（NN）噪声消除来消除杂散声音信号，如来自心脏、肠道和环境噪声的杂散声音信号。到目前为止，核心诊断模块能够识别肺音和非肺音，正常肺音和异常肺音，并将喘息和爆裂音识别为不同疾病的指标。
Respiratory sounds are of significance as they provide valuable information on the health of the respiratory system. Sounds emanating from the respiratory system are uneven, and vary significantly from one individual to another and for the same individual over time. In and of themselves they are not a direct proof of an ailment, but rather an inference that one exists. Auscultation diagnosis is an art/skill that is acquired and honed by practice; hence it is common to seek confirmation using invasive and potentially harmful imaging diagnosis techniques like X-rays. This research focuses on developing an automated auscultation diagnostic system that overcomes the limitations inherent in traditional auscultation techniques. The system uses a front end sound signal filtering module that uses adaptive Neural Networks (NN) noise cancellation to eliminate spurious sound signals like those from the heart, intestine, and ambient noise. To date, the core diagnosis module is capable of identifying lung sounds from non-lung sounds, normal lung sounds from abnormal ones, and identifying wheezes from crackles as indicators of different ailments.