Department of Flight Simulator Innovations, Military Institute of Aviation Medicine, ul. Krasińskiego 54/56, Warszawa 01-755, Poland.
Department of Flight Simulator Innovations, Military Institute of Aviation Medicine, ul. Krasińskiego 54/56, Warszawa 01-755, Poland.
Comput Methods Programs Biomed. 2019 Aug;177:31-38. doi: 10.1016/j.cmpb.2019.05.014. Epub 2019 May 17.
Monitoring of changes in respiratory rate provides information on a patient's psychophysical state. This paper presents a respiratory rate detection method based on analysis of signals from a fiber Bragg grating (FBG)-based sensor.
The detection method is based on a system of software blocks that identify notches in the signal waveforms, determine their parameters, and then transmit them to the classifier, which decides which of them are the characteristic waves of the respiratory cycle. The classifier of respiratory waves was developed by means of machine learning methods and using the training data obtained from 10 volunteers (7 males, 3 females, age: 41.1 ± 8.28 years, weight: 73.6 ± 15.25 kg, height 173.5 ± 6.43 cm), who were lying in the tube of a 3-Tesla magnetic resonance imaging (MRI) scanner.
In the verification study, aimed at assessing the performance of the method for detecting respiratory rate, 15 subjects (14 males, 1 female, age: 20.2 ± 3.00 years, weight: 75.47 ± 10.58 kg, height 179.13 ± 6.27 cm) were involved. Clinically satisfactory results of respiratory rate detection were obtained: root mean square error of 1.48 rpm and the limits of agreement at -2.73 rpm and 3.04 rpm. The results indicate a high efficiency of the classifier, i.e., sensitivity: 96.50 ± 3.44%, precision: 95.42 ± 2.84%, and accuracy: 92.99 ± 3.37%.
The all-dielectric sensor acquires the respiration curve and the proposed scheme of computation enables for extracting respiratory rate automatically and continuously. This scheme based on machine learning procedures will be integrated into a system to facilitate non-invasive continuous monitoring of MRI patients.
呼吸率的监测可提供患者的身心状态信息。本文提出了一种基于光纤布拉格光栅（FBG）传感器信号分析的呼吸率检测方法。
该检测方法基于软件模块系统，该系统可识别信号波形中的凹口，确定其参数，然后将其传输到分类器，分类器将决定哪些是呼吸周期的特征波。呼吸波分类器是通过机器学习方法和使用从 10 名志愿者（7 名男性，3 名女性，年龄：41.1±8.28 岁，体重：73.6±15.25kg，身高 173.5±6.43cm）获得的训练数据开发的，志愿者们躺在 3T 磁共振成像（MRI）扫描仪的管中。
在验证研究中，为评估检测呼吸率的方法的性能，涉及 15 名受试者（14 名男性，1 名女性，年龄：20.2±3.00 岁，体重：75.47±10.58kg，身高 179.13±6.27cm）。获得了令人满意的呼吸率检测结果：均方根误差为 1.48rpm，协议范围在-2.73rpm 和 3.04rpm 之间。结果表明分类器的效率很高，即灵敏度：96.50±3.44%，精度：95.42±2.84%，准确性：92.99±3.37%。
全介质传感器获取呼吸曲线，所提出的计算方案可自动连续提取呼吸率。这种基于机器学习过程的方案将被整合到一个系统中，以方便对 MRI 患者进行非侵入式连续监测。
