用机器学习分析家庭呼吸数据，实现无医生参与的呼吸状况自动判断。
Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment
- 基于压力、流量和胸腹围变化数据，训练随机森林等模型识别呼吸类型。
- 结合呼吸频率特征后，随机森林分类准确率最高，达92.5%。
- 适合关注居家慢病监测、可穿戴设备开发的临床与工程人员。
呼吸系统疾病对全球健康构成重大负担，当前诊断与管理主要依赖专科临床检测。本研究旨在开发基于机器学习的算法，支持接受持续气道正压通气（CPAP）治疗的患者在家中进行呼吸疾病监测与评估。数据来自30名健康成人，在正常、喘息和深呼吸三种呼吸状态下采集了呼吸压力、流量及动态胸腹围数据。采用随机森林分类器、逻辑回归和支撑向量机（SVM）等多种机器学习模型进行呼吸类型预测。结果显示，随机森林分类器在引入呼吸频率作为特征时表现最优。这些发现表明，基于AI的呼吸监测系统有望将呼吸评估从临床环境转移至家庭场景，提升可及性与患者自主性。未来工作将针对更大、更多样的人群验证模型，并探索更多机器学习方法。
原文摘要 · Abstract (English)
Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure (CPAP) therapy. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements under three breathing conditions: normal, panting, and deep breathing. Various machine learning models, including the random forest classifier, logistic regression, and support vector machine (SVM), were trained to predict breathing types. The random forest classifier demonstrated the highest accuracy, particularly when incorporating breathing rate as a feature. These findings support the potential of AI-driven respiratory monitoring systems to transition respiratory assessments from clinical settings to home environments, enhancing accessibility and patient autonomy. Future work involves validating these models with larger, more diverse populations and exploring additional machine learning techniques.
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