The described application of granular computing is motivated because cardiovascular disease (CVD) remains a major killer globally. There is increasing evidence that abnormal respiratory patterns might contribute to the development and progression of CVD. Consequently, a method that would support a physician in respiratory pattern evaluation should be developed. Group decision-making, tri-way reasoning, and rough set–based analysis were applied to granular computing. Signal attributes and anthropomorphic parameters were explored to develop prediction models to determine the percentage contribution of periodic-like, intermediate, and normal breathing patterns in the analyzed signals. The proposed methodology was validated employing k-nearest neighbor (k-NN) and UMAP (uniform manifold approximation and projection). The presented approach applied to respiratory pattern evaluation shows that median accuracies in a considerable number of cases exceeded 0.75. Overall, parameters related to signal analysis are indicated as more important than anthropomorphic features. It was also found that obesity characterized by a high WHR (waist-to-hip ratio) and male sex were predisposing factors for the occurrence of periodic-like or intermediate patterns of respiration. It may be among the essential findings derived from this study. Based on classification measures, it may be observed that a physician may use such a methodology as a respiratory pattern evaluation-aided method.

由于心血管疾病 (CVD) 仍然是全球范围内的主要杀手，因此所描述的粒度计算应用是有动机的。越来越多的证据表明异常呼吸模式可能导致 CVD 的发生和发展。因此，应该开发一种支持医生进行呼吸模式评估的方法。群体决策、三向推理和基于粗糙集的分析被应用于粒计算。探索了信号属性和拟人化参数以开发预测模型，以确定分析信号中周期性、中间和正常呼吸模式的百分比贡献。所提出的方法使用 k 最近邻 (k-NN) 和 UMAP（均匀流形近似和投影）进行了验证。所提出的应用于呼吸模式评估的方法表明，在相当多的案例中，准确度中值超过了 0.75。总的来说，与信号分析相关的参数比拟人特征更重要。还发现以高 WHR（腰臀比）和男性为特征的肥胖是出现周期性或中间呼吸模式的诱发因素。这可能是这项研究得出的重要发现之一。基于分类措施，可以观察到医生可以使用诸如呼吸模式评估辅助方法这样的方法。与信号分析相关的参数比拟人特征更重要。还发现以高 WHR（腰臀比）和男性为特征的肥胖是出现周期性或中间呼吸模式的诱发因素。这可能是这项研究得出的重要发现之一。基于分类措施，可以观察到医生可以使用诸如呼吸模式评估辅助方法这样的方法。与信号分析相关的参数比拟人特征更重要。还发现以高 WHR（腰臀比）和男性为特征的肥胖是出现周期性或中间呼吸模式的诱发因素。这可能是这项研究得出的重要发现之一。基于分类措施，可以观察到医生可以使用诸如呼吸模式评估辅助方法这样的方法。