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

Addison, Paul; Smit, Philip; Jacquel, Dominique; Borg, Ulf

doi:10.1007/s10877-019-00417-6

Cited by 11 publications

(19 citation statements)

References 20 publications

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“…We can see that the ROI in this case was fitted to the chest and abdominal region. Figure 4 b shows the respiratory pattern obtained during a breathe-down study conducted as part of a pulse oximetry trial [ 17 ]. In this study, volunteers were fitted with a face mask in order to adjust their FiO 2 levels using a mixture of nitrogen and oxygen to induce desaturation.…”

Section: Deriving Respiratory Information Using a Depth Cameramentioning

confidence: 99%

“…A 14-person protocolised breath-down lab study by our own group at Medtronic [ 17 ] measured continuous RR during an acute hypoxic challenge using a Kinect V2 depth camera. The hypoxic challenge consisted of oxygen saturation reduction steps, down from 100% to 70% which elicited a wide range of respiratory rates.…”

Section: Respiratory Ratementioning

confidence: 99%

“…The ROI is a rectangular subset of the chest. (Reprinted from [ 17 ].) ( c ) A simulated apnea signal.…”

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

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

Addison

Smit

et al. 2021

Sensors

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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.

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Section: Deriving Respiratory Information Using a Depth Cameramentioning

confidence: 99%

Section: Respiratory Ratementioning

confidence: 99%

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

Addison

Smit

et al. 2021

Sensors

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“…In addition, they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The first widely available depth sensor was MS Kinect for Xbox 360 and MS Kinect for Xbox One, which was both successfully tested for complex monitoring of selected biomedical features [1][2][3][4][5][6][7]. They formed an inexpensive replacement for conventional devices.…”

Section: Introductionmentioning

confidence: 99%

“…In general, the use of image and depth sensors [1,7,[9][10][11][12] allow sleep monitoring and 3D modeling of the chest and abdomen volume changes during breaths as an alternative to conventional spirometry with the use of red-green-blue (RGB) cameras [13,14], infrared cameras [15,16], or Doppler multi-radar systems [17] in addition to ultrasonic and biomotion sensors [18][19][20][21]. The paper provides motivation for further studies of methods related to big data processing problems, dimensionality reduction, principal component analysis, and parallel signal processing to increase the speed, accuracy, and reliability of the results.…”

Section: Introductionmentioning

confidence: 99%

Sleep Apnea Detection with Polysomnography and Depth Sensors

Schätz

Procházka

Kuchynka

et al. 2020

Sensors

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This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20–35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an F 1 score of 92.2% Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

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

Addison

Smit

Jacquel

et al. 2021

J Clin Monit Comput

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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.

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

Cited by 11 publications

References 20 publications

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

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

Sleep Apnea Detection with Polysomnography and Depth Sensors

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

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