论文标题
使用遥控传感器呼吸模式监测
Breathing Pattern Monitoring using Remote Sensors
论文作者
论文摘要
呼吸是最重要的身体功能之一，因为它为其提供了氧气，这对于能量产生至关重要。另外，去除二氧化碳会积极调节酸碱水平，这对于人体的生理功能至关重要。由于其与许多其他身体功能的密切联系，呼吸也可以用作广泛的医疗状况的指标，乍一看与呼吸无关。神经，心脏病，炎症，代谢甚至心理状况都以呼吸模式出现。因此，能够自动和毫不显眼地对其进行分类，可以使成本效益的监视系统不断评估患者的健康状况。在这项工作中，提出并比较了热摄像机和RGB摄像机的多种呼吸信号算法。提出和评估了一种用于提取多种呼吸特征的新型算法。使用一个与一台多类支持向量机，这些功能用于对多种呼吸模式进行分类，准确性高达95.79％。
Breathing is one of the most important body functions because it provides it with oxygen, which is vital for energy production. In addition, the removal of carbon dioxide actively regulates the acid-base level, which is essential for the physiological function of the body. Due to its close connection with many other body functions, respiration can also be used as an indicator for a wide spectrum of medical conditions, which at first glance have little to do with breathing. Neurological, cardiological, inflammatory, metabolic, and even psychological conditions symptomatically show up in breathing patterns. Hence, being able to classify them automatically and unobtrusively, can allow cost-effective monitoring systems to continuously assess the health of a patient. In this work, multiple respiratory signal-extraction algorithms for thermal and RGB cameras are presented and compared. A novel algorithm for the extraction of multiple respiratory features is presented and evaluated. Using a one vs. one multiclass support vector machine, these features were used to classify a wide range of respiratory patterns with an accuracy of up to 95.79%.
