Continuous non‐contact respiratory rate and tidal volume monitoring using a Depth Sensing Camera

Abstract

The monitoring of respiratory parameters is important across many areas of care within the hospital. Here we report on the performance of a depth-sensing camera system for the continuous non-contact monitoring of Respiratory Rate (RR) and Tidal Volume (TV), where these parameters were compared to a ventilator reference. Depth sensing data streams were acquired and processed over a series of runs on a single volunteer comprising a range of respiratory rates and tidal volumes to generate depth-based respiratory rate (RRdepth) and tidal volume (TVdepth) estimates. The bias and root mean squared difference (RMSD) accuracy between RRdepth and the ventilator reference, RRvent, across the whole data set was found to be -0.02 breaths/min and 0.51 breaths/min respectively. The least squares fit regression equation was determined to be: RRdepth = 0.96 × RRvent + 0.57 breaths/min and the resulting Pearson correlation coefficient, R, was 0.98 (p < 0.001). Correspondingly, the bias and root mean squared difference (RMSD) accuracy between TVdepth and the reference TVvent across the whole data set was found to be − 0.21 L and 0.23 L respectively. The least squares fit regression equation was determined to be: TVdepth = 0.79 × TVvent—0.01 L and the resulting Pearson correlation coefficient, R, was 0.92 (p < 0.001). In conclusion, a high degree of agreement was found between the depth-based respiration rate and its ventilator reference, indicating that RRdepth is a promising modality for the accurate non-contact respiratory rate monitoring in the clinical setting. In addition, a high degree of correlation between depth-based tidal volume and its ventilator reference was found, indicating that TVdepth may provide a useful monitor of tidal volume trending in practice. Future work should aim to further test these parameters in the clinical setting.

Cardio‐respiratory and phenotypic rescue of dystrophin/utrophin‐deficient mice by combination therapy

“…Notably, the kyphosis index significantly rose in PMO‐GM‐treated DKO mice compared to untreated DKO controls (Fig 4C), showing the prevention of spinal deformation and subsequent premature death. As most DMD patients suffer from respiratory failure (Lo Mauro & Aliverti, 2016), we measured the tidal volume, a parameter reflecting respiratory function (Addison et al , 2021), and found a significant elevation in DKO mice treated with PMO‐GM compared to untreated DKO controls (Fig 4D). Consistently, EF and FS rates, and cardiac output and stroke volume, parameters for cardiac function (Magnani et al , 2018), were significantly increased in PMO‐GM‐treated DKO mice compared to untreated DKO controls (Fig 4E and F), with substantially reduced cardiac fibrosis (Fig 4G).…”

Depth-Based Measurement of Respiratory Volumes: A Review

“…In the last step, cropping ensures anatomical correctness. A semi-automatic algorithm, the flood fill method, for selecting the ROI is used in [51].…”

“…Due to the sudden COVID-19 outbreak and subsequent restrictions, a study by Addison et al 2021 [51] had to be stopped prematurely. Results with only one subject show a mean deviation of −213 ± 85 mL.…”

Non-Contact Breathing Rate Estimation Using Machine Learning with an Optimized Architecture

“…Finally, RGB images and ambient light are used to track the motion due to respiration on the thorax [20][21][22][23]. Besides, in recent works, a depth-sensing camera was used to estimate the RR and the tidal volume (TV) [24,25]. The principle is based on the computation of the depth value of the chest or abdomen during the respiratory cycle.…”