AI 中文总结
研究探讨用呼吸音记录和机器学习检测儿科哮喘的可行性，利用预训练自监督语音模型提取特征，结合传统机器学习分类器，Wav2Vec 2.0与基于直方图的梯度提升结合表现最佳，为儿科哮喘检测提供非侵入性方法。
AI 中文摘要
由于呼吸症状重叠、时间限制以及幼儿肺功能测试可行性有限，急诊科准确诊断儿科哮喘仍具挑战性。本研究探讨使用呼吸音记录和机器学习在急诊科检测儿科哮喘的可行性。从31名儿科患者（10名哮喘患者，21名非哮喘患者）的六个胸部位置收集30秒呼吸音，使用预训练的自监督语音表示模型提取特征，并将患者年龄和性别纳入特征表示。使用患者级分层组5折交叉验证和留一患者验证对传统机器学习分类器进行训练和评估。在评估方法中，Wav2Vec 2.0与基于直方图的梯度提升相结合表现最强且最一致，在两种评估协议下准确率为0.84，灵敏度为0.80，特异性为0.86，F1分数为0.76。这些发现表明预训练的自监督音频表示为现实世界急诊科环境中的儿科哮喘检测提供了一种有前景的非侵入性方法。
英文摘要
Accurate diagnosis of pediatric asthma in emergency departments remains challenging due to overlapping respiratory symptoms, time constraints, and the limited feasibility of pulmonary function testing in young children. This study investigates the feasibility of pediatric asthma detection in the emergency department using breath sound recordings and machine learning. Thirty-second breath sounds were collected from six chest locations in 31 pediatric patients (10 asthmatic, 21 non-asthmatic) and analyzed using pretrained self-supervised speech representation models (HuBERT, WavLM, and Wav2Vec 2.0) for feature extraction, with patient age and sex incorporated into the feature representations. Conventional machine learning classifiers were trained and evaluated using patient-level stratified group 5-fold cross-validation and leave-one-patient-out validation to ensure the generalizability of the findings. Among the evaluated approaches, Wav2Vec 2.0 combined with histogram-based gradient boosting achieved the strongest and most consistent performance, yielding an accuracy of 0.84, sensitivity of 0.80, specificity of 0.86, and F1-score of 0.76 under both evaluation protocols. The consistency of performance across validation strategies suggests promising generalization to unseen patients. These findings suggest that pretrained self-supervised audio representations offer a promising, non-invasive approach for pediatric asthma detection in real-world emergency department settings, where objective respiratory assessment is often limited.
CommentsAccepted for publication at EMBC2026
