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.

Video-based heart rate monitoring across a range of skin pigmentations during an acute hypoxic challenge.

The robust monitoring of heart rate from the video-photoplethysmogram (video-PPG) during challenging conditions requires new analysis techniques. The work reported here extends current research in this area by applying a motion tolerant algorithm to extract high quality video-PPGs from a cohort of subjects undergoing marked heart rate changes during a hypoxic challenge, and exhibiting a full range of skin pigmentation types. High uptimes in reported video-based heart rate (HRvid) were targeted, while retaining high accuracy in the results. Ten healthy volunteers were studied during a double desaturation hypoxic challenge. Video-PPGs were generated from the acquired video image stream and processed to generate heart rate. HRvid was compared to the pulse rate posted by a reference pulse oximeter device (HRp). Agreement between video-based heart rate and that provided by the pulse oximeter was as follows: Bias = - 0.21 bpm, RMSD = 2.15 bpm, least squares fit gradient = 1.00 (Pearson R = 0.99, p < 0.0001), with a 98.78% reporting uptime. The difference between the HRvid and HRp exceeded 5 and 10 bpm, for 3.59 and 0.35% of the reporting time respectively, and at no point did these differences exceed 25 bpm. Excellent agreement was found between the HRvid and HRp in a study covering the whole range of skin pigmentation types (Fitzpatrick scales I-VI), using standard room lighting and with moderate subject motion. Although promising, further work should include a larger cohort with multiple subjects per Fitzpatrick class combined with a more rigorous motion and lighting protocol.

Running wavelet archetype aids the determination of heart rate from the video photoplethysmogram during motion.

The extraction of heart rate from a video-based biosignal during motion using a novel wavelet-based ensemble averaging method is described. Running Wavelet Archetyping (RWA) allows for the enhanced extraction of pulse information from the time-frequency representation, from which a video-based heart rate (HRvid) can be derived. This compares favorably to a reference heart rate derived from a pulse oximeter.

Video-Based Physiologic Monitoring During an Acute Hypoxic Challenge: Heart Rate, Respiratory Rate, and Oxygen Saturation.

BACKGROUND: The physiologic information contained in the video photoplethysmogram is well documented. However, extracting this information during challenging conditions requires new analysis techniques to capture and process the video image streams to extract clinically useful physiologic parameters. We hypothesized that heart rate, respiratory rate, and oxygen saturation trending can be evaluated accurately from video information during acute hypoxia. METHODS: Video footage was acquired from multiple desaturation episodes during a porcine model of acute hypoxia using a standard visible light camera. A novel in-house algorithm was used to extract photoplethysmographic cardiac pulse and respiratory information from the video image streams and process it to extract a continuously reported video-based heart rate (HRvid), respiratory rate (RRvid), and oxygen saturation (SvidO2). This information was then compared with HR and oxygen saturation references from commercial pulse oximetry and the known rate of respiration from the ventilator. RESULTS: Eighty-eight minutes of data were acquired during 16 hypoxic episodes in 8 animals. A linear mixed-effects regression showed excellent responses relative to a nonhypoxic reference signal with slopes of 0.976 (95% confidence interval [CI], 0.973-0.979) for HRvid; 1.135 (95% CI, 1.101-1.168) for RRvid, and 0.913 (95% CI, 0.905-0.920) for video-based oxygen saturation. These results were obtained while maintaining continuous uninterrupted vital sign monitoring for the entire study period. CONCLUSIONS: Video-based monitoring of HR, RR, and oxygen saturation may be performed with reasonable accuracy during acute hypoxic conditions in an anesthetized porcine hypoxia model using standard visible light camera equipment. However, the study was conducted during relatively low motion. A better understanding of the effect of motion and the effect of ambient light on the video photoplethysmogram may help refine this monitoring technology for use in the clinical environment.

Video monitoring of oxygen saturation during controlled episodes of acute hypoxia.

A method for extracting video photoplethysmographic information from an RGB video stream is tested on data acquired during a porcine model of acute hypoxia. Cardiac pulsatile information was extracted from the acquired signals and processed to determine a continuously reported oxygen saturation (SvidO2). A high degree of correlation was found to exist between the video and a reference from a pulse oximeter. The calculated mean bias and accuracy across all eight desaturation episodes were -0.03% (range: -0.21% to 0.24%) and accuracy 4.90% (range: 3.80% to 6.19%) respectively. The results support the hypothesis that oxygen saturation trending can be evaluated accurately from a video system during acute hypoxia.