利用MobileNetV2自动检测来自电子听诊器和手机的异常呼吸声

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-10-01 DOI:10.1016/j.bbe.2023.11.001
Ximing Liao , Yin Wu , Nana Jiang , Jiaxing Sun , Wujian Xu , Shaoyong Gao , Jun Wang , Ting Li , Kun Wang , Qiang Li
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引用次数: 0

摘要

听诊是一种传统的临床检查方法,使用听诊器快速评估气道异常,由于其实时性,无创性和易于操作的性质,仍然有价值。计算机呼吸音分析(CRSA)的最新进展为记录、编辑和比较呼吸音提供了可量化的方法,也使人工智能模型的训练能够充分挖掘听诊的潜力。然而,现有的声音分析模型往往需要复杂的计算,导致处理时间延长,计算和内存需求高。此外,可用数据库的有限多样性和范围限制了可重复性和稳健性,主要依赖于主要从高加索人收集的小样本数据集。为了克服这些限制,我们利用电子听诊器和手机开发了一个新的中国成人呼吸声数据库LD-DF RSdb。通过招募145名参与者,收集了9584个高质量的录音,其中包括6435个正常声音,2782个噼啪声,208个喘息声和159个混合声音。随后,我们利用轻量级的神经网络架构MobileNetV2对四种类型的呼吸声音进行自动分类,取得了令人满意的整体性能,AUC为0.8923。本研究证明了在CRSA中使用移动电话、电子听诊器和MobileNetV2的可行性和潜力。提出的方法提供了一种方便和有前途的方法,以加强整体呼吸系统疾病的管理,并可能有助于解决医疗资源差距。
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Automated detection of abnormal respiratory sound from electronic stethoscope and mobile phone using MobileNetV2

Auscultation, a traditional clinical examination method using a stethoscope to quickly assess airway abnormalities, remains valuable due to its real-time, non-invasive, and easy-to-perform nature. Recent advancements in computerized respiratory sound analysis (CRSA) have provided a quantifiable approach for recording, editing, and comparing respiratory sounds, also enabling the training of artificial intelligence models to fully excavate the potential of auscultation. However, existing sound analysis models often require complex computations, leading to prolonged processing times and high calculation and memory requirements. Moreover, the limited diversity and scope of available databases limits reproducibility and robustness, mainly relying on small sample datasets primarily collected from Caucasians. In order to overcome these limitations, we developed a new Chinese adult respiratory sound database, LD-DF RSdb, using an electronic stethoscope and mobile phone. By enrolling 145 participants, 9,584 high quality recordings were collected, containing 6,435 normal sounds, 2,782 crackles, 208 wheezes, and 159 combined sounds. Subsequently, we utilized a lightweight neural network architecture, MobileNetV2, for automated categorization of the four types of respiratory sounds, achieving an appreciable overall performance with an AUC of 0.8923. This study demonstrates the feasibility and potential of using mobile phones, electronic stethoscopes, and MobileNetV2 in CRSA. The proposed method offers a convenient and promising approach to enhance overall respiratory disease management and may help address healthcare resource disparities.

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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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