Monitoring Respiratory Rate Continuously Without Attaching a Sensor During a Challenging Ramped Protocol.

INTRODUCTION: Respiratory rate (RR) is a crucial vital sign in patient monitoring and is often the best marker of the deterioration of a sick patient. It can be used to help diagnose numerous medical conditions and has been demonstrated to be an independent predictor of patient outcomes in various critical care settings and is incorporated in many clinical early warning scores. Here, we report on the performance of depth-camera-based system for the noncontact monitoring of RR during a ramped RR protocol. The ramped breathing protocol was developed specifically to test the relatively rapid changes in rates, which include clinically important low and high ranges of RRs. MATERIALS AND METHODS: We performed a series of experimental runs with healthy volunteers who were instructed to breathe over a wide range of RRs, where the rates were ramped up from 4 breaths/min to 50 breaths/min then back down to 4 breaths/min in a series of ramped steps. Depth information was acquired from the scene and used to determine a respiratory rate (RRdepth), and this was compared to capnograph or spirometer respiratory rate reference (RRref). A total of 9,482 contemporaneous data pairs (RRdepth, RRref) were collected during the study for comparison. RESULTS: A Pearson correlation coefficient of 0.995 was achieved and a line of best fit given by RRdepth = 0.99 × RRref + 0.36 breaths/min. The overall root mean squared difference (RMSD) across the runs was 1.29 breaths/min with a corresponding bias of 0.16 breaths/min, respectively. The associated Bland-Altman analysis found limits of agreement of -2.45 and 2.75 breaths/min. When the data were subdivided according to low, medium, and high RRs, corresponding to ≤10, >10 to 20, and >20 breaths/min, the RMSD accuracies were found to be 0.94, 1.34, and 1.55 breaths/min, respectively. CONCLUSIONS: The technology performed well, exhibiting an RMSD accuracy well within our target of 3 breaths/min, both across the whole range and across each individual subrange. In summary, our results indicate the potential viability of continuous noncontact monitoring for the determination of RR over a clinically relevant range.

Robust Non-Contact Monitoring of Respiratory Rate using a Depth Camera.

PURPOSE: Respiratory rate (RR) is one of the most common vital signs with numerous clinical uses. It is an important indicator of acute illness and a significant change in RR is often an early indication of a potentially serious complication or clinical event such as respiratory tract infection, respiratory failure and cardiac arrest. Early identification of changes in RR allows for prompt intervention, whereas failing to detect a change may result in poor patient outcomes. Here, we report on the performance of a depth-sensing camera system for the continuous non-contact 'touchless' monitoring of Respiratory Rate. METHODS: Seven healthy subjects undertook a range of breathing rates from 4 to 40 breaths-per-minute (breaths/min). These were set rates of 4, 5, 6, 8, 10, 15, 20, 25, 30, 35 and 40 breaths/min. In total, 553 separate respiratory rate recordings were captured across a range of conditions including body posture, position within the bed, lighting levels and bed coverings. Depth information was acquired from the scene using an Intel D415 RealSenseTM camera. This data was processed in real-time to extract depth changes within the subject's torso region corresponding to respiratory activity. A respiratory rate RRdepth was calculated using our latest algorithm and output once-per-second from the device and compared to a reference. RESULTS: An overall RMSD accuracy of 0.69 breaths/min with a corresponding bias of -0.034 was achieved across the target RR range of 4-40 breaths/min. Bland-Altman analysis revealed limits of agreement of -1.42 to 1.36 breaths/min. Three separate sub-ranges of low, normal and high rates, corresponding to < 12, 12-20, > 20 breaths/min, were also examined separately and each found to demonstrate RMSD accuracies of less than one breath-per-minute. CONCLUSIONS: We have demonstrated high accuracy in performance for respiratory rate based on a depth camera system. We have shown the ability to perform well at both high and low rates which are clinically important.

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

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.

Noncontact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature.

There is considerable interest in the noncontact monitoring of patients as it allows for reduced restriction of patients, the avoidance of single-use consumables and less patient-clinician contact and hence the reduction of the spread of disease. A technology that has come to the fore for noncontact respiratory monitoring is that based on depth sensing camera systems. This has great potential for the monitoring of a range of respiratory information including the provision of a respiratory waveform, the calculation of respiratory rate and tidal volume (and hence minute volume). Respiratory patterns and apneas can also be observed in the signal. Here we review the ability of this method to provide accurate and clinically useful respiratory information.

Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera.

Respiratory rate is a well-known to be a clinically important parameter with numerous clinical uses including the assessment of disease state and the prediction of deterioration. It is frequently monitored using simple spot checks where reporting is intermittent and often prone to error. We report here on an algorithm to determine respiratory rate continuously and robustly using a non-contact method based on depth sensing camera technology. The respiratory rate of 14 healthy volunteers was studied during an acute hypoxic challenge where blood oxygen saturation was reduced in steps to a target 70% oxygen saturation and which elicited a wide range of respiratory rates. Depth sensing data streams were acquired and processed to generate a respiratory rate (RRdepth). This was compared to a reference respiratory rate determined from a capnograph (RRcap). The bias and root mean squared difference (RMSD) accuracy between RRdepth and the reference RRcap was found to be 0.04 bpm and 0.66 bpm respectively. The least squares fit regression equation was determined to be: RRdepth = 0.99 × RRcap + 0.13 and the resulting Pearson correlation coefficient, R, was 0.99 (p < 0.001). These results were achieved with a 100% reporting uptime. In conclusion, excellent agreement was found between RRdepth and RRcap. Further work should include a larger cohort combined with a protocol to further test algorithmic performance in the face of motion and interference typical of that experienced in the clinical setting.