Pereira Carina B, Blazek Vladimir, Venema Boudewijn, Leonhardt Steffen
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:1381-1384. doi: 10.1109/EMBC.2016.7590965.
Scientific studies have demonstrated that an atypical respiratory rate (RR) is frequently one of the earliest and major indicators of physiological distress. However, it is also described in the literature as "the neglected vital parameter", mainly due to shortcomings of clinical available monitoring techniques, which require attachment of sensors to the patient's body. The current paper introduces a novel approach that uses multisensor data fusion for an enhanced RR estimation in thermal videos. It considers not only the temperature variation around nostrils and mouth, but the upward and downward movement of both shoulders. In order to analyze the performance of our approach, two experiments were carried out on five healthy candidates. While during phase A, the subjects breathed normally, during phase B they simulated different breathing patterns. Thoracic effort was the gold standard elected to validate our algorithm. Our results show an excellent agreement between infrared thermography (IRT) and ground truth. While in phase A a mean correlation of 0.983 and a root-mean-square error of 0.240 bpm (breaths per minute) was obtained, in phase B they hovered around 0.995 and 0.890 bpm, respectively. In sum, IRT may be a promising clinical alternative to conventional sensors. Additionally, multisensor data fusion contributes to an enhancement of RR estimation and robustness.
科学研究表明，非典型呼吸频率（RR）常常是生理窘迫最早出现的主要指标之一。然而，它在文献中也被描述为“被忽视的重要参数”，主要是由于临床现有监测技术存在缺陷，这些技术需要将传感器附着在患者身体上。本文介绍了一种新颖的方法，该方法利用多传感器数据融合来增强对热成像视频中呼吸频率的估计。它不仅考虑鼻孔和嘴巴周围的温度变化，还考虑双肩的上下运动。为了分析我们方法的性能，对五名健康受试者进行了两项实验。在A阶段，受试者正常呼吸，而在B阶段，他们模拟不同的呼吸模式。胸廓运动被选为验证我们算法的金标准。我们的结果显示红外热成像（IRT）与真实情况高度吻合。在A阶段，平均相关性为0.983，均方根误差为0.240次/分钟（每分钟呼吸次数），而在B阶段，它们分别徘徊在0.995和0.890次/分钟左右。总之，红外热成像可能是传统传感器的一种有前景的临床替代方法。此外，多传感器数据融合有助于提高呼吸频率估计的准确性和稳健性。
