中国全科医学 ›› 2024, Vol. 27 ›› Issue (36): 4598-4608.DOI: 10.12114/j.issn.1007-9572.2023.0863

• 全科医疗工具与方法研究 • 上一篇    下一篇

肺部听诊音数据库建库技术及方法研究

张冬莹1,2, 叶培韬3, 李洽胜2, 简文华2, 梁振宇2, 郑劲平2,*()   

  1. 1.999078 澳门特别行政区,澳门科技大学医学院
    2.510120 广东省广州市,广州医科大学附属第一医院 广州呼吸健康研究院 国家呼吸系统疾病临床医学研究中心
    3.510310 广东省广州市,广东省第二人民医院
  • 收稿日期:2024-01-22 修回日期:2024-04-10 出版日期:2024-12-20 发布日期:2024-09-19
  • 通讯作者: 郑劲平

  • 作者贡献:

    张冬莹提出肺音研究思路,设计研究方案,研究命题的提出、设计,包括肺音听诊对象分组,伪湿啰音的智能识别和目标导向的研究思路(智能判读听诊器的推广应用导向),以及项目研究过程中专利申报的思路等负责论文起草,负责最终版本修订,对论文负责;张冬莹、李洽胜、简文华、梁振宇负责研究对象甄别入组,组织研究过程的实施,负责质量控制;张冬莹、叶培韬负责数据收集、采集、清洗和统计学分析、绘制图表等;郑劲平为研究项目顾问,指导研究方案及论文修改。

  • 基金资助:
    澳门科技大学发展基金项目(0070/2020/A2)

Study of Techniques and Methods for Building a Database of Lung Auscultation Sounds

ZHANG Dongying1,2, YE Peitao3, LI Qiasheng2, JIAN Wenhua2, LIANG Zhenyu2, ZHENG Jinping2,*()   

  1. 1. Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
    2. The First Affiliated Hospital of Guangzhou Medical University/Guangzhou Institute of Respiratory Health/National Clinical Research Center for Respiratory Diseases, Guangzhou 510120, China
    3. Guangdong Second Provincial General Hospital, Guangzhou 510310, China
  • Received:2024-01-22 Revised:2024-04-10 Published:2024-12-20 Online:2024-09-19
  • Contact: ZHENG Jinping

摘要: 当前无论是物理听诊器亦或是电子听诊器的肺音听诊结果仍然主要依靠医生专业的听诊鉴别能力,尚未能够实现智能诊断判读。当患者在家受到肺部疾病影响时,无法自行发现肺部异常而耽误治疗;当处于呼吸道传染病救治过程中,入耳式的听诊器容易受到污染而造成院内感染。尽管听诊音包含了丰富的健康状态信息,由于缺乏标准化的采集方法、分类标准和分析工具,使得听诊音的客观分析和应用在实践中受到了限制。本研究通过采用统一的听诊音采集设备和流程进行肺部听诊音数据采集、整理、数据库设计,使用软件MatlabR2017a进行数据管理和分析,建立了健康群体和肺部疾病患者群体的肺部听诊音数据库,制订一套标准的听诊音分类、标注规范、音频特征信号参数,构建一个用于存储、管理和分析肺部听诊音数据的系统,为肺部疾病的筛查、监测以及医学人工智能应用转化等相关研究提供重要的数据支持。本研究为肺部听诊音音频数据库建库积累了经验,为音频类数据库管理和分析提供有益的参考和借鉴,为支持后续医学人工智能辅助听诊应用于肺部疾病筛查与监测奠定基础,具有重要的医学价值和实际应用意义。

关键词: 肺疾病, 肺部听诊音, 音频数据库, 支持向量机, 特征识别, 数据分析

Abstract:

Currently, the results of lung sound auscultation with either physical or electronic stethoscopes still rely mainly on the doctor's professional auscultation identification ability, which has not yet been able to realise intelligent diagnosis and interpretation. When patients are affected by lung diseases at home, they are unable to detect lung abnormalities on their own and delay treatment; when they are in the process of rescue and treatment of respiratory infectious diseases, in-ear stethoscopes are easily contaminated and cause nosocomial infections. Although stethoscopic sounds contain a wealth of information about health status, the lack of standardised collection methods, classification criteria and analysis tools has limited the objective analysis and application of stethoscopic sounds in practice. In this study, the data collection, arrangement and database design of the lung auscultation sound were carried out by using the unified auscultation sound collection equipment and process. The study used the software MetlabR2017a for data management and analysis to create a database of lung auscultation sounds in a healthy group and a group of patients with lung disease. A database of lung auscultation sounds was established for healthy groups and groups of patients with lung diseases. A standard set of classification of auscultatory tones, labelling specifications, audio characteristic signal parameters were developed. Building a system for storing, managing and analysing lung auscultation sound data to provide important data support for research related to the screening and monitoring of lung diseases and the translation of medical artificial intelligence applications. The study accumulated the experience of building an audio database of lung auscultation sounds, provided a useful reference for the management and analysis of the audio database, and laied the foundation for supporting the subsequent application of medical artificial intelligence-assisted auscultation in the screening and monitoring of lung diseases, which was of great medical value and practical application.

Key words: Lung diseases, Lung auscultation sound, Audio database, Support vector machine, Feature recognition, Data analysis

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