IEEE J Biomed Health Inform. 2019 Jan;23(1):208-217. doi: 10.1109/JBHI.2018.2817258. Epub 2018 Mar 22.
In this paper, a noncontact breathing disorder recognition system has been proposed for identifying irregular breathing patterns. The proposed system consists of a Doppler radar-based sensor module and a machine-learning-based breathing disorder recognition module. A custom-designed 2.4-GHz continuous wave digital-IF Doppler radar is utilized as the radar sensor module to accurately capture the time-domain breathing waveform. Then, a recognition module is designed with selected features and optimized classifiers. Four sets of experiments have been carried out to evaluate the proposed system comprehensively. For the laboratorial experiments, the proposed system achieves 94.7% classification accuracy using the linear support vector machine classifier with seven selected features. Results of clinical experiments demonstrate the feasibility of long-term breathing disorder recognition with good accuracy and robustness, and illustrate the potential of the proposed solution for the auxiliary diagnosis of diseases.
本文提出了一种基于非接触式雷达的呼吸障碍识别系统，用于识别不规则的呼吸模式。该系统由基于多普勒雷达的传感器模块和基于机器学习的呼吸障碍识别模块组成。我们使用自行设计的 2.4GHz 连续波数字中频多普勒雷达作为雷达传感器模块，以准确地获取时域呼吸波形。然后，我们设计了一个带有精选特征和优化分类器的识别模块。我们进行了四组实验来全面评估所提出的系统。在实验室实验中，我们使用带有七个精选特征的线性支持向量机分类器，实现了 94.7%的分类准确率。临床实验的结果表明，该系统具有良好的准确性和鲁棒性，能够实现长时间的呼吸障碍识别，这说明了该解决方案在疾病辅助诊断方面的潜力。
