Various studies have already focused on human physiological rates, including those involving metabolism, hormones, the autonomic nervous system and their interactions 1. The estimation of human physiological rates is of interest for clinical research and health monitoring with regard to disease prevention 2. The analysis of these rates help to better understand the interactions between human physiological systems from a macroscopic point of view 3, 4, 5, 6. In this context, measuring breathing rates without a mouthpiece is needed to monitor, treat and/or prevent various health conditions such as chronic obstructive pulmonary disease, post-thoracic surgery, ankylosis spondylitis, breathing kinematics in the context of spinal cord injuries 7, 8 and more recently for pulmonary complications in severe COVID-19 cases 9, 10.
Variation in various human biological rhythms, such as respiration, hearth rate, blood pressure, are currently estimated using non-invasive devices 11. Spirometry is the standard method for monitoring breathing. For specific pathological care, monitoring the respiration rate without a mouthpiece has become mandatory. Over the past decade, methods based on electrocardiograms (ECGs) and plethysmograms have become accepted for estimate the breathing rate. For clinical monitoring, the global fast Fourier transform algorithm has been recognized for its accuracy and sensitivity in the identification of the main breathing and heart rates 11 12. The usefulness of these two methods for monitoring breathing has been attributed to the relationship between thoracic motion and hearth rate, known as respiratory sinus arrhythmia 12. Accordingly, optical fibre sensors are increasingly used to asses breathing rates because they can be associated with magnetic resonance imaging 13, 14, 15. However, this technique has a relatively high limit of agreement (LOA) ranging from ± 0,45 to ± 2 cpm 13, 14. Similarly contact ultrasonic sensors monitor breathing in sleep apnoea syndromes 16, 17 or in conjunction with emotional state such as anger or happiness. This method has a Kappa coefficient of k = 0.38 compared with oronasal flow 17. Infrared thermography can estimate normal breathing patterns 18, 19, 20, because nasal and torso thermal signatures show a high cross-correlation (r = 0.98) 21. Thermal signatures have an LOA of ± 0.5 s compared with inductance plethysmography 22. Based on variation in chest wall velocity, a triaxial accelerometer indirectly measures breathing and hearth rate variability 23, and these physiological signals are highly correlated (r = 0.96) with those estimated from chest deformation gauges and pulse oximeters 24. However, accelerometers show time-cumulative errors compared with spirometer measurements 25, and they have an LOA of ± 4 cpm compared with ECGs 26. Finally, although all these devices are more or less accurate in estimating human breathing rates, they do not provide information on the biomechanics associated with breathing rates.
Structured light plethysmography offers a better estimate of three-dimensional chest wall motion and its frequency 27, 28. This marker-less method can be used to reconstruct chest wall movements in clinical applications 29, 30. Respiratory volume monitored by structured light plethysmography correlates (R2 > 0.91) with spirometer measurements 27. However to our knowledge, the accuracy and reliability of structured light plethysmography have not been investigated. Based on infrared cameras, optoelectronic plethysmography (OEP) is a motion-capture method that provides an accurate and reliable three-dimensional reconstruction of chest wall movements. The first OEP was developed 30 years ago 31, 32 based on video recordings of 32 passive motion-capture markers placed on the subject’s torso to measure three-dimensional chest volumes and the variability in chest wall surface motion and to estimate nine chest volumes. In these conditions, the 3D accuracy of the OEP system was SD = 0.06 mm 31. Increasing the number of markers in the vertical and horizontal planes (some studies have used up to 89 markers) can improve accuracy 32, 33. However, due to its potential clinical applications, OEP was adapted with 24 markers and 9 virtual markers on the subject’s back to study breathing in newborns 34. Recent studies have shown that increasing the number of markers always improved accuracy in OEP. According to 35, bias and limit of agreement were lower when OEP is associated with 30 markers (bias = 0.056 l and LOA ± 0.35 l) compared with 89 markers (bias = 0.16 l and LOA ± 0.4 l). Moreover, OEP associated with 16 markers seems to be sufficient to monitor tidal volume in spontaneous breathing 36, and other studies have shown that OEP associated with less than 16 markers can be used to estimate breathing rates and specific biomechanical parameters, such as the ratio between thoracic and abdominal breathing movements (13 markers; Kaneko & Horie 37) or sternal angle variation (6 markers; Gaillard et al. 38). To our knowledge, only Shafiq & Veluvolu 39 have used OEP associated with 16 markers to monitor breathing and cardiac frequencies, simultaneously according to the chest wall marker positions in Alnowan et al. 40, with 12 of them presenting characteristic signals due to their proximal position on the diaphragm 39. These 12 markers better predict diaphragm movements using abdominal chest volumes 41 and provide estimates of breathing rates. Only two studies 42, 34 have compared the accuracy of OEP compared with the standard method on more than 10 subjects, (10 adults and 20 infants, respectively). Both studies used more than 16 retro-reflexives markers placed on the anterior torso (respectively 45 markers for adults and 24 markers for infants). To our knowledge, no study has assessed the accuracy and reliability of breathing rate estimates from a 12-marker OEP system compared with those from a standard method.
The aim of the present study was to evaluate the accuracy and reliability of monitoring breathing rates using an OEP system and an ECG device compared with the standard spirometer. We tested whether controlled breathing rates estimated using OEP and ECG can reproduce spirometer measurements with the same accuracy and reliability.