Detection of respiratory rate using a classifier of waves in the signal from a FBG-based vital signs sensor.

BACKGROUND AND OBJECTIVE

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.

METHODS

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.

RESULTS

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%.

CONCLUSION

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.