Respiratory airflow signals provide critical insight into breathing mechanics, yet conventional analysis methods remain limited in their ability to characterize the internal structure of individual breaths. Traditional approaches treat airflow as a quasi-periodic signal and rely on global descriptors such as tidal volume or peak flow, obscuring sub-breath events that reflect neuromuscular coordination and compensatory breathing strategies. This study introduces a parametric framework for decomposing inspiratory airflow into a small number of time-localized components with explicit amplitude, onset time, and duration parameters. Unlike spectral or data-adaptive methods, the proposed approach employs physiologically grounded basis functions, Half-Sine, Gaussian, and Beta, to represent intrabreath waveform morphology through constrained nonlinear optimization. Evaluation across 8,276 breaths demonstrates high reconstruction accuracy (mean squared error $<$ 0.001 for four-component models) and robust parameter precision under moderate noise. Component-derived features describing sub-breath timing and coordination improved classification of cognitive fatigue states arising from cognitive-respiratory competition by up to 30.7% in Matthews correlation coefficient compared with classical respiratory metrics. These results establish that modeling airflow as a sum of parameterized, time-localized primitives provides an interpretable and precise foundation for quantifying intrabreath organization, compensatory breathing dynamics, and respiratory motor control adaptation under cognitive-respiratory dual-task demands.

翻译：呼吸气流信号为呼吸力学提供了关键洞察，然而传统分析方法在表征单次呼吸内部结构方面仍存在局限性。常规方法将气流视为准周期信号，并依赖潮气量或峰值流量等全局描述符，从而掩盖了反映神经肌肉协调和代偿性呼吸策略的亚呼吸事件。本研究引入了一种参数化框架，将吸气气流分解为少量具有明确幅值、起始时间和持续时间的局部时间分量。与频谱或数据自适应方法不同，本方法采用基于生理学的基函数——半正弦、高斯和贝塔函数，通过约束非线性优化来表征呼吸内波形形态。对8,276次呼吸的评估表明，该方法具有高重构精度（四分量模型均方误差<0.001）以及在适度噪声下的稳健参数精度。源自分量的特征描述了亚呼吸时序与协调性，相比经典呼吸指标，对由认知-呼吸竞争引起的认知疲劳状态分类的马修斯相关系数提升高达30.7%。这些结果表明，将气流建模为参数化、时间局部化基元的和，为量化呼吸内组织、代偿性呼吸动力学以及认知-呼吸双重任务需求下的呼吸运动控制适应提供了可解释且精确的基础。