約 10 秒的靜音循環動畫，畫面包含聽診與呼吸音波形等元素，並有局部動作與鏡頭推近。肺音分析的難點之一是區分吸氣與呼氣等呼吸相；邊界標註會影響後續特徵分析。相關研究提出了呼吸音自動時間標籤分割方法並完成驗證。
圖背後的故事
背景：在呼吸系統疾病中，呼吸音聽診對具體疾病診斷具有重要作用，但聽診者個人經驗及環境因素可能影響判斷並造成診斷錯誤。因此，準確、有效地獲取和分析呼吸音有助於呼吸系統疾病的診斷與治療。目的：建立用於呼吸音頻數據可視化和數字化的分析方法，並驗證其區分不同基礎疾病的能力。方法：研究採集 2023 年 6 至 12 月上海長海醫院全科醫學科住院患者的呼吸音，經納排標準篩選後納入 84 名患者。在安靜環境中，患者暴露胸部、平臥，使用電子聽診器在胸部六個標誌位置採集聲音；將音頻導入分析工具進行分割及特徵提取。分析步驟包括區分心音與呼吸音、分割呼吸音、判定吸氣與呼氣相，並使用團隊開發的工具進行自動分割編碼。結果：研究標準化了 84 名患者的呼吸音，分割出多個呼吸週期；根據標籤信息定位並分割呼吸週期後，計算各段振幅特徵的均值與標準差，結果顯示不同疾病之間存在差異。結論：該算法平臺可將呼吸音分爲吸氣和呼氣相，並比較不同基礎疾病之間的差異，爲呼吸音聽診及其可視化展示提供客觀依據。
論文摘要（中文譯文 · 原文）
中文譯文 摘要 背景 在呼吸系統疾病領域，聽診中呼吸音的利用對特定疾病的診斷至關重要。但聽診過程中，個人經驗與環境因素可能影響判斷，導致診斷誤差。因此，準確有效地獲取並分析呼吸音，對呼吸系統疾病的診療有積極作用。目的 建立一種呼吸音頻數據可視化與數字化的分析方法，並驗證其區分不同基礎疾病的能力。方法 研究收集2023年6月至12月上海長海醫院全科醫學科收治患者的呼吸音，按納入與排除標準嚴格篩查後共納入84例患者。研究流程爲：在安靜環境中以電子聽診器採集患者肺音，患者暴露胸部、平臥，於胸部六個標誌位置採集聲音數據；將採集的音頻導入分析工具進行分段與特徵提取，具體包括心音與呼吸音的區分、呼吸音分段、吸氣相與呼氣相判定，以及使用團隊開發的工具進行自動分段編碼。結果 我們對84例患者的呼吸音進行標準化處理並分割出多個呼吸週期；在基於標籤信息對呼吸週期定位與分段後，計算呼吸週期各段幅度特徵的均值與標準差，結果顯示不同基礎疾病之間存在差異。結論 該穩健的算法平臺能夠將呼吸音相應地分割爲吸氣相與呼氣相，進而比較不同基礎疾病之間的差異，爲呼吸音聽診與呼吸音可視化展示提供客觀依據。
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.