In global healthcare, respiratory diseases are a leading cause of mortality, underscoring the need for rapid and accurate diagnostics. To advance rapid screening techniques via auscultation, our research focuses on employing one of the largest publicly available medical database of respiratory sounds to train multiple machine learning models able to classify different health conditions. Our method combines Empirical Mode Decomposition (EMD) and spectral analysis to extract physiologically relevant biosignals from acoustic data, closely tied to cardiovascular and respiratory patterns, making our approach apart in its departure from conventional audio feature extraction practices. We use Power Spectral Density analysis and filtering techniques to select Intrinsic Mode Functions (IMFs) strongly correlated with underlying physiological phenomena. These biosignals undergo a comprehensive feature extraction process for predictive modeling. Initially, we deploy a binary classification model that demonstrates a balanced accuracy of 87% in distinguishing between healthy and diseased individuals. Subsequently, we employ a six-class classification model that achieves a balanced accuracy of 72% in diagnosing specific respiratory conditions like pneumonia and chronic obstructive pulmonary disease (COPD). For the first time, we also introduce regression models that estimate age and body mass index (BMI) based solely on acoustic data, as well as a model for gender classification. Our findings underscore the potential of this approach to significantly enhance assistive and remote diagnostic capabilities.

翻译：在全球医疗体系中，呼吸系统疾病是导致死亡的主要原因之一，凸显了快速准确诊断的迫切需求。为推进通过听诊实现快速筛查技术，本研究采用公开可用的最大呼吸音医学数据库之一，训练多种机器学习模型以实现不同健康状态的分类。我们的方法融合经验模态分解（EMD）与频谱分析，从声学数据中提取与心血管及呼吸模式密切相关的生理相关生物信号，这一技术路线区别于传统音频特征提取方法。通过功率谱密度分析与滤波技术，我们筛选出与潜在生理现象强相关的本征模态函数（IMF），并对这些生物信号进行综合特征提取以构建预测模型。初始阶段，我们部署的二分类模型在区分健康与患病个体时展现出87%的平衡准确率；随后采用六分类模型诊断特定呼吸疾病（如肺炎与慢性阻塞性肺疾病COPD）时达到72%的平衡准确率。本研究首次引入仅基于声学数据估算年龄与身体质量指数（BMI）的回归模型，以及性别分类模型。研究结果证实，该方法具有显著增强辅助诊断与远程诊疗能力的潜力。