Respiratory diseases are becoming a severe health threat. To prevent exacerbation with early diagnosis, there is an urgent need for developing a respiratory function assay with ease of access. Tidal breathing pattern reflects a combination of the existing lung condition and the physiological demand. However, the interpretations of breath pattern remain underexplored. In this study, lung simulator with various pathological parameters was used to reconstruct the breath pattern of patients with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD). Breath pattern was recorded using two flow sensors. Three machine learning algorithms, including convolutional neural network (CNN), long short-term memory (LSTM) and support vector machine (SVM), were applied for disease identification. Results showed algorithmic analysis can achieve over 80% accuracy, and two levels of obstructive severity of COPD can be determined. With the assistance of algorithms, similar ...

中文翻译：

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使用算法辅助的便携式气流传感器识别呼吸功能障碍

呼吸系统疾病正成为严重的健康威胁。为了通过早期诊断防止病情加重，迫切需要开发一种易于操作的呼吸功能测定方法。潮气呼吸方式反映了现有肺部状况和生理需求的结合。但是，关于呼吸模式的解释仍未得到充分研究。在这项研究中，使用具有各种病理参数的肺模拟器来重建患有慢性阻塞性肺疾病（COPD）和间质性肺疾病（ILD）的患者的呼吸模式。使用两个流量传感器记录呼吸模式。包括卷积神经网络（CNN），长短期记忆（LSTM）和支持向量机（SVM）在内的三种机器学习算法被用于疾病识别。结果表明，算法分析可以达到80％以上的准确性，并且可以确定COPD的两种阻塞性严重程度。在算法的帮助下，类似...