约 10 秒的静音循环动画，画面包含听诊与呼吸音波形等元素，并有局部动作与镜头推近。肺音分析的难点之一是区分吸气与呼气等呼吸相；边界标注会影响后续特征分析。相关研究提出了呼吸音自动时间标签分割方法并完成验证。
图背后的故事
背景：在呼吸系统疾病中，呼吸音听诊对具体疾病诊断具有重要作用，但听诊者个人经验及环境因素可能影响判断并造成诊断错误。因此，准确、有效地获取和分析呼吸音有助于呼吸系统疾病的诊断与治疗。目的：建立用于呼吸音频数据可视化和数字化的分析方法，并验证其区分不同基础疾病的能力。方法：研究采集 2023 年 6 至 12 月上海长海医院全科医学科住院患者的呼吸音，经纳排标准筛选后纳入 84 名患者。在安静环境中，患者暴露胸部、平卧，使用电子听诊器在胸部六个标志位置采集声音；将音频导入分析工具进行分割及特征提取。分析步骤包括区分心音与呼吸音、分割呼吸音、判定吸气与呼气相，并使用团队开发的工具进行自动分割编码。结果：研究标准化了 84 名患者的呼吸音，分割出多个呼吸周期；根据标签信息定位并分割呼吸周期后，计算各段振幅特征的均值与标准差，结果显示不同疾病之间存在差异。结论：该算法平台可将呼吸音分为吸气和呼气相，并比较不同基础疾病之间的差异，为呼吸音听诊及其可视化展示提供客观依据。
论文摘要（中文译文 · 原文）
中文译文 摘要 背景 在呼吸系统疾病领域，听诊中呼吸音的利用对特定疾病的诊断至关重要。但听诊过程中，个人经验与环境因素可能影响判断，导致诊断误差。因此，准确有效地获取并分析呼吸音，对呼吸系统疾病的诊疗有积极作用。目的 建立一种呼吸音频数据可视化与数字化的分析方法，并验证其区分不同基础疾病的能力。方法 研究收集2023年6月至12月上海长海医院全科医学科收治患者的呼吸音，按纳入与排除标准严格筛查后共纳入84例患者。研究流程为：在安静环境中以电子听诊器采集患者肺音，患者暴露胸部、平卧，于胸部六个标志位置采集声音数据；将采集的音频导入分析工具进行分段与特征提取，具体包括心音与呼吸音的区分、呼吸音分段、吸气相与呼气相判定，以及使用团队开发的工具进行自动分段编码。结果 我们对84例患者的呼吸音进行标准化处理并分割出多个呼吸周期；在基于标签信息对呼吸周期定位与分段后，计算呼吸周期各段幅度特征的均值与标准差，结果显示不同基础疾病之间存在差异。结论 该稳健的算法平台能够将呼吸音相应地分割为吸气相与呼气相，进而比较不同基础疾病之间的差异，为呼吸音听诊与呼吸音可视化展示提供客观依据。
ABSTRACT Background In the field of respiratory system diseases, the utilization of respiratory sounds in auscultation plays a crucial role in the specific disease diagnosis. However, during the process of auscultation, the personal experiences and environmental factors may affect the decision making, leading to diagnostic errors. Therefore, to accurately and effectively obtain and analyze respiratory sounds can be positively contribute to the diagnosis and treatment of respiratory system diseases. Objectives Our aim was to develop an analytical method for the visualization and digitization of respiratory audio data, and to validate its capability to differentiate between various background diseases. Methods This study collected the respiratory sounds of patients admitted to the Department of General Medicine of Shanghai Changhai Hospital from June to December 2023. After strict screening according to the inclusion and exclusion criteria, a total of 84 patients were included. The research process includes using an electronic stethoscope to collect lung sounds from patients in a quiet environment. The patients expose their chests and lie flat. Sound data are collected at six landmark positions on the chest. The collected audio files are imported into an analysis tool for segmentation and feature extraction. Specific analysis methods include distinguishing heart sounds and respiratory sounds, segmenting respiratory sounds, determining the inspiratory and expiratory phases, and using a tool developed by the team for automatic segmentation encoding. Results We standardized the respiratory sounds of 84 patients and segmented multiple respiratory cycles. Following the localization and segmentation of the respiratory cycles based on label information, we calculated the average and standard deviation of the amplitude features for each segment of the respiratory cycle. The results indicated differences among various diseases. Conclusions The robust algorithm platform is capable of segmenting the respiratory sounds into inhale and exhale phases accordingly, then comparing the difference between different background diseases. This method provides objective evidence for the auscultation of respiratory sounds and visual display of breath sounds.