AI 中文总结
针对传统肺活量测定的问题，提出SpiRadar框架，通过整合生理预处理、多项式变换和稀疏优化方法，经儿科队列严格验证，性能优于其他非接触方法，确立了非接触肺活量测定法的临床可行性，尤其适用于儿科哮喘监测和远程护理。
AI 中文摘要
慢性呼吸道疾病影响全球数亿人，肺活量测定是肺功能评估的金标准。但传统肺活量测定依赖口器和鼻夹，带来不适与技术挑战，尤其在儿科人群中。本研究提出SpiRadar，一种基于雷达的非接触肺活量测定综合框架，消除身体接触需求，实现准确曲线重建、临床参数估计和支气管扩张剂反应评估。关键贡献包括：整合生理预处理、特征相关多项式变换和稀疏优化的方法框架；对39名6至18岁受试者进行58次肺活量测定试验的严格验证；临床级性能优于其他非接触方法。这些结果确立了非接触肺活量测定方法的临床可行性，对儿科哮喘监测和远程患者护理有特殊前景。
英文摘要
Chronic respiratory diseases affect hundreds of millions of people worldwide, with spirometry serving as the gold standard for pulmonary function assessment. However, conventional spirometry's reliance on mouthpieces and nose clips creates discomfort and technical challenges that can compromise test quality, particularly in pediatric populations where cooperation difficulties are amplified. This study presents SpiRadar, a comprehensive radar-based framework for non-contact spirometry that eliminates physical contact requirements while enabling accurate curve reconstruction, clinical parameter estimation, and bronchodilator response (BDR) assessment. Our key contributions include: (1) a methodological framework integrating physiologically-motivated preprocessing for robust signal extraction, a feature-dependent polynomial transformation linking radar-measured thoracic displacement to spirometric volume curves, and sparse optimization enabling generalization to unseen subjects without individual calibration; (2) a rigorous validation on a pediatric cohort of 39 subjects (ages 6-18 years) undergoing 58 spirometry trials, including healthy children and asthma patients tested pre- and post-bronchodilator, using subject-level leave-one-out cross-validation; (3) clinical-grade performance outperforming alternative non-contact methods, achieving accurate curve reconstruction (mean RMSE: 0.23 +- 0.12 L) and strong correlations (above 0.84) for key spirometric parameters. BDR classification achieved 89.5% accuracy with balanced sensitivity (90.9%) and specificity (87.5%). These results establish clinical feasibility for our non-contact spirometry approach, with particular promise for pediatric asthma monitoring and remote patient care.
