Surface imaging for real-time patient respiratory function assessment in intensive care.

Intensive Care Unit noncontact monitoring respiratory function surface imaging

Journal

Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746

Informations de publication

Date de publication:
Jan 2021
Historique:
received: 27 04 2020
revised: 08 10 2020
accepted: 20 10 2020
pubmed: 30 10 2020
medline: 1 5 2021
entrez: 29 10 2020
Statut: ppublish

Résumé

Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients. The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P < 0.001 and r = 0.99; P < 0.001) in the mannequin recordings and (r = 0.95, P < 0.001 and r = 0.90, P < 0.001) for patients. This study describes and evaluates a novel noncontact monitoring system suitable for continuous monitoring of key respiratory parameters for disease assessment of critically ill patients.

Identifiants

pubmed: 33118190
doi: 10.1002/mp.14557
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

142-155

Subventions

Organisme : SATT
Organisme : Université de Bretagne Occidentale (UBO)

Informations de copyright

© 2020 American Association of Physicists in Medicine.

Références

Brochard L, Martin GS, Blanch L, et al. Clinical review: respiratory monitoring in the ICU-a consensus of 16. Crit Care. 2012;16:219.
Bajwa EK, Malhotra A, Thompson BT. Methods of monitoring shock. In: Seminars in Respiratory and Critical Care Medicine. Vol 25. Copyright© 2004 by Thieme Medical Publishers, Inc., 333 Seventh Avenue, New; 2004:629-644.
Weinhouse GL. Pulmonary artery catheterization: Indications and complications. UpToDate Walth MA UpToDate Inc Retrieved This Link June; 2010;11:2011.
Nguyen LS, Squara P. Non-invasive monitoring of cardiac output in critical care medicine. Front Med. 2017;4:200.
Parker JC, Hernandez LA, Peevy KJ. Mechanisms of ventilator-induced lung injury. Crit Care Med. 1993;21:131-143.
Slutsky AS, Ranieri VM. Ventilator-induced lung injury. N Engl J Med. 2013;369:2126-2136.
Ricard J-D, Dreyfuss D, Saumon G. Ventilator-induced lung injury. Eur Respir J. 2003;22:2s-9s.
Tobin MJ. Principles and practice of mechanical ventilation. Shock. 2006;26:426.
Grieco DL, Menga LS, Eleuteri D, Antonelli M. Patient self-inflicted lung injury: implications for acute hypoxemic respiratory failure and ARDS patients on non-invasive support. Minerva Anestesiol. 2019;85:1014-1023.
Miller MR, Hankinson J, Brusasco V, et al. Standardisation of spirometry. Eur Respir J. 2005;26:319-338.
Criée CP, Sorichter S, Smith HJ, et al. Body plethysmography-its principles and clinical use. Respir Med. 2011;105:959-971.
Sackner MA, Watson H, Belsito AS, et al. Calibration of respiratory inductive plethysmograph during natural breathing. J Appl Physiol. 1989;66:410-420.
Kim H, Kim J-Y, Im C-H. Fast and robust real-time estimation of respiratory rate from photoplethysmography. Sensors. 2016;16:1494.
Zhang X, Ding Q. Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra fusion. Biomed Signal Process Control. 2017;35:1-7.
Houtveen JH, Groot PF, de Geus EJ. Validation of the thoracic impedance derived respiratory signal using multilevel analysis. Int J Psychophysiol. 2006;59:97-106.
Seppa V-P, Viik J, Hyttinen J. Assessment of pulmonary flow using impedance pneumography. IEEE Trans Biomed Eng. 2010;57:2277-2285.
Hmeidi H, Motamedi-Fakhr S, Chadwick E, et al. Tidal breathing parameters measured using structured light plethysmography in healthy children and those with asthma before and after bronchodilator. Physiol Rep. 2017;5:e13168.
Tang WW, Tong W. Measuring impedance in congestive heart failure: current options and clinical applications. Am Heart J. 2009;157:402-411.
Brüllmann G, Fritsch K, Thurnheer R, Bloch KE. Respiratory monitoring by inductive plethysmography in unrestrained subjects using position sensor-adjusted calibration. Respiration. 2010;79:112-120.
Moody GB, Mark RG, Zoccola A, Mantero S. Derivation of respiratory signals from multi-lead ECGs. Comput Cardiol. 1985;1985:113-116.
Castaneda D, Esparza A, Ghamari M, Soltanpur C, Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int J Biosens Bioelectron. 2018;4:195.
Gilbert R, Auchincloss JH Jr, Brodsky J, Boden W. Changes in tidal volume, frequency, and ventilation induced by their measurement. J Appl Physiol. 1972;33:252-254.
Massaroni C, Lopes DS, Lo Presti D, Schena E, Silvestri S. Contactless monitoring of breathing patterns and respiratory rate at the pit of the neck: a single camera approach. J Sens. 2018;2018:1-13.
Birsan N, Munteanu D-P. Non-contact cardiopulmonary monitoring algorithm for a 24 GHz Doppler radar. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2012:3227-3230.
Lubecke OB, Ong P-W, Lubecke VM. 10 GHz Doppler radar sensing of respiration and heart movement. In: Proceedings of the IEEE 28th Annual Northeast Bioengineering Conference (IEEE Cat. No. 02CH37342). IEEE; 2002:55-56.
Scalise L, Ercoli I, Marchionni P, Tomasini EP. Measurement of respiration rate in preterm infants by laser Doppler vibrometry. In: 2011 IEEE International Symposium on Medical Measurements and Applications. IEEE; 2011:657-666.
Arlotto P, Grimaldi M, Naeck R, Ginoux J-M. An ultrasonic contactless sensor for breathing monitoring. Sensors. 2014;14:15371-15386.
Min SD, Yoon DJ, Yoon SW, Yun YH, Lee M. A study on a non-contacting respiration signal monitoring system using Doppler ultrasound. Med Biol Eng Comput. 2007;45:1113-1119.
Abbas AK, Heimann K, Jergus K, Orlikowsky T, Leonhardt S. Neonatal non-contact respiratory monitoring based on real-time infrared thermography. Biomed Eng Online. 2011;10:93.
Hu M-H, Zhai G-T, Li D, Fan Y-Z, Chen X-H, Yang X-K. Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement. J Biomed Opt. 2017;22:036006.
AL-Khalidi FQ, Saatchi R, Burke D, Elphick H, Tan S. Respiration rate monitoring methods: a review. Pediatr Pulmonol. 2011;46:523-529.
Kranjec J, Beguš S, Geršak G, Drnovšek J. Non-contact heart rate and heart rate variability measurements: a review. Biomed Signal Process Control. 2014;13:102-112.
Al-Naji A, Gibson K, Lee S-H, Chahl J. Monitoring of cardiorespiratory signal: principles of remote measurements and review of methods. IEEE Access. 2017;5:15776-15790.
Abbas AK, Heiman K, Orlikowsky T, Leonhardt S. Non-contact respiratory monitoring based on real-time IR-thermography. In: World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany. Springer; 2009:1306-1309.
Schaller C, Penne J, Hornegger J. Time-of-flight sensor for respiratory motion gating. Med Phys. 2008;35:3090-3093.
Penne J, Schaller C, Hornegger J, Kuwert T. Robust real-time 3D respiratory motion detection using time-of-flight cameras. Int J Comput Assist Radiol Surg. 2008;3:427-431.
Yu M-C, Wu H, Liou J-L, Lee M-S, Hung Y-P. Breath and Position Monitoring during Sleeping with a Depth Camera. In: HEALTHINF; 2012:12-22.
Martinez M, Stiefelhagen R. Breath rate monitoring during sleep using near-IR imagery and PCA. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012). IEEE; 2012:3472-3475.
Procházka A, Schätz M, Centonze F, Kuchyňka J, Vyšata O, Vališ M. Extraction of breathing features using MS Kinect for sleep stage detection. Signal Image Video Process. 2016;10:1279-1286.
Benetazzo F, Freddi A, Monteriù A, Longhi S. Respiratory rate detection algorithm based on RGB-D camera: theoretical background and experimental results. Healthc Technol Lett. 2014;1:81-86.
Li Q, Cao H, Li Y, Lu Y. How do you breathe-a non-contact monitoring method using depth data. In: 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services (Healthcom). IEEE; 2017:1-6.
Xia J, Siochi RA. A real-time respiratory motion monitoring system using KINECT: proof of concept. Med Phys. 2012;39:2682-2685.
Yu M-C, Liou J-L, Kuo S-W, Lee M-S, Hung Y-P. Noncontact respiratory measurement of volume change using depth camera. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE; 2012:2371-2374.
Aoki H, Miyazaki M, Nakamura H, et al. Proceedings of SICE annual conference (SICE). IEEE. 2012;2012:614-618.
Harte JM, Golby CK, Acosta J, et al. Chest wall motion analysis in healthy volunteers and adults with cystic fibrosis using a novel Kinect-based motion tracking system. Med Biol Eng Comput. 2016;54:1631-1640.
Sharp C, Soleimani V, Hannuna S, et al. Toward respiratory assessment using depth measurements from a time-of-flight sensor. Front Physiol. 2017;8:65.
Rehouma H, Noumeir R, Bouachir W, Jouvet P, Essouri S. 3D imaging system for respiratory monitoring in pediatric intensive care environment. Comput Med Imaging Graph. 2018;70:17-28.
Kohoutek TK, Mautz R, Donaubauer A. Real-time indoor positioning using range imaging sensors. In: Real-Time Image and Video Processing 2010. Vol 7724. International Society for Optics and Photonics; 2010:77240K.
Blanc N, Oggier T, Gruener G, Weingarten J, Codourey A, Seitz P. Miniaturized smart cameras for 3d-imaging in real-time [mobile robot applications]. In: SENSORS, 2004 IEEE. IEEE; 2004:471-474.
Lachat E, Macher H, Mittet MA, Landes T, Grussenmeyer P. First experiences with Kinect v2 sensor for close range 3D modelling. Int Arch Photogramm Remote Sens Spat Inf Sci. 2015;40:93.
Keller M, Lefloch D, Lambers M, Izadi S, Weyrich T, Kolb A. Real-time 3d reconstruction in dynamic scenes using point-based fusion. In: 2013 International Conference on 3D Vision-3DV 2013. IEEE; 2013:1-8.
Forster F. Real-time range imaging for human-machine interfaces. Published online; 2005.
Breuer P, Eckes C, Müller S. Hand gesture recognition with a novel IR time-of-flight range camera-a pilot study. In: International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications. Berlin: Springer; 2007:247-260.
Bauer S, Seitel A, Hofmann H, et al. Real-time range imaging in health care: a survey. In: Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications. Berlin: Springer; 2013:228-254.
Gallo L, Placitelli AP, Ciampi M. Controller-free exploration of medical image data: Experiencing the Kinect. In: 2011 24th International Symposium on Computer-Based Medical Systems (CBMS). IEEE; 2011:1-6.
Bert C, Metheany KG, Doppke K, Chen GT. A phantom evaluation of a stereo-vision surface imaging system for radiotherapy patient setup. Med Phys. 2005;32:2753-2762.
Walter F, Freislederer P, Belka C, Heinz C, Söhn M, Roeder F. Evaluation of daily patient positioning for radiotherapy with a commercial 3D surface-imaging system (CatalystTM). Radiat Oncol. 2016;11:154.
Gilles M, Fayad H, Miglierini P, et al. Patient positioning in radiotherapy based on surface imaging using time of flight cameras. Med Phys. 2016;43:4833-4841.
Chiu C-Y, Thelwell M, Senior T, Choppin S, Hart J, Wheat J. Comparison of depth cameras for three-dimensional reconstruction in medicine. Proc Inst Mech Eng [H]. 2019;233:938-947.
Langmann B, Hartmann K, Loffeld O. Depth Camera Technology Comparison and Performance Evaluation. In: ICPRAM (2). Citeseer; 2012:438-444.
Yao H, Ge C, Xue J, Zheng N. A high spatial resolution depth sensing method based on binocular structured light. Sensors. 2017;17:805.
Lin B-S, Su M-J, Cheng P-H, Tseng P-J, Chen S-J. Temporal and spatial denoising of depth maps. Sensors. 2015;15:18506-18525.
Sarbolandi H, Lefloch D, Kolb A. Kinect range sensing: structured-light versus time-of-flight kinect. Comput Vis Image Underst. 2015;139:1-20.
Foix Salmerón S, Alenyà Ribas G, Torras C. Exploitation of time-of-flight (ToF) cameras. Published online; 2010.
Fankhauser P, Bloesch M, Rodriguez D, Kaestner R, Hutter M, Siegwart R. Kinect v2 for mobile robot navigation: Evaluation and modeling. In: 2015 International Conference on Advanced Robotics (ICAR). IEEE; 2015:388-394.
Wasenmüller O, Stricker D. Comparison of kinect v1 and v2 depth images in terms of accuracy and precision. In: Asian Conference on Computer Vision. Berlin: Springer; 2016:34-45.
Yang L, Zhang L, Dong H, Alelaiwi A, El Saddik A. Evaluating and improving the depth accuracy of Kinect for Windows v2. IEEE Sens J. 2015;15:4275-4285.
Briva A, Gaiero C. Lung protection: an intervention for tidal volume reduction in a teaching intensive care unit. Rev Bras Ter Intensiva. 2016;28:373-379.
Rodriguez R, Hern HG Jr. Topic in review: an approach to critically ill patients. West J Med. 2001;175:392.
Cretikos MA, Bellomo R, Hillman K, Chen J, Finfer S, Flabouris A. Respiratory rate: the neglected vital sign. Med J Aust. 2008;188:657-659.
Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified early warning score in medical admissions. QJM. 2001;94:521-526.
Liu H, Allen J, Zheng D, Chen F. Recent development of respiratory rate measurement technologies. Physiol Meas. Published online; 2019.
Ponikowski P, Francis DP, Piepoli MF, et al. Enhanced ventilatory response to exercise in patients with chronic heart failure and preserved exercise tolerance: marker of abnormal cardiorespiratory reflex control and predictor of poor prognosis. Circulation. 2001;103:967-972.
Rambaud-Althaus C, Althaus F, Genton B, D’Acremont V. Clinical features for diagnosis of pneumonia in children younger than 5 years: a systematic review and meta-analysis. Lancet Infect Dis. 2015;15:439-450.
Maharaj R, Raffaele I, Wendon J. Rapid response systems: a systematic review and meta-analysis. Crit Care. 2015;19:254.
Hodgetts TJ, Kenward G, Vlachonikolis IG, Payne S, Castle N. The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. Resuscitation. 2002;54:125-131.
Liu H, Guo S, Liu H, et al. The best body spot to detect the vital capacity from the respiratory movement data obtained by the wearable strain sensor. J Phys Ther Sci. 2018;30:586-589.
Johnston CR, Krishnaswamy N, Krishnaswamy G. The Hoover’s sign of pulmonary disease: molecular basis and clinical relevance. Clin Mol Allergy. 2008;6:8.
Terragni PP, Rosboch G, Tealdi A, et al. Tidal hyperinflation during low tidal volume ventilation in acute respiratory distress syndrome. Am J Respir Crit Care Med. 2007;175:160-166.
Gattinoni L, Caironi P, Pelosi P, Goodman LR. What has computed tomography taught us about the acute respiratory distress syndrome? Am J Respir Crit Care Med. 2001;164:1701-1711.
Frerichs I, Amato MB, Van Kaam AH, et al. Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group. Thorax. 2017;72:83-93.
Garcia-Pachon E. Paradoxical movement of the lateral rib margin (Hoover sign) for detecting obstructive airway disease. Chest. 2002;122:651-655.
Shotton J, Fitzgibbon A, Cook M, et al. Real-time human pose recognition in parts from single depth images. In: CVPR 2011. IEEE; 2011:1297-1304.
Sun K, Xiao B, Liu D, Wang J. Deep high-resolution representation learning for human pose estimation. ArXiv Prepr ArXiv190209212. Published online; 2019.
Lin T-Y, Maire M, Belongie S, et al. Microsoft coco: common objects in context. In: European Conference on Computer Vision. Berlin: Springer; 2014;740-755.
Cao Z, Simon T, Wei S-E, Sheikh Y. Realtime multi-person 2d pose estimation using part affinity fields. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017:7291-7299.
Nazir S, Rihana S, Visvikis D, Fayad H. Kinect V2 surface filtering during gantry motion for radiotherapy applications. Med Phys. 2018;45:1400-1407.
D’Agostino RB. Goodness-of-Fit-Techniques. Vol. 68. Boca Raton: CRC Press; 1986.
Corti A, Giancola S, Mainetti G, Sala R. A metrological characterization of the Kinect V2 time-of-flight camera. Robot Auton Syst. 2016;75:584-594.
Sevrin L, Noury N, Abouchi N, et al. Detection of collaborative activity with Kinect depth cameras. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE; 2016:5973-5976.
Lee J, Hong M, Ryu S. Sleep monitoring system using kinect sensor. Int J Distrib Sens Netw. 2015;11:875371.
Akbarnia BA, Yazici M, Thompson GH. The Growing Spine: Management of Spinal Disorders in Young Children. Berlin: Springer Science & Business Media; 2010.

Auteurs

Souha Nazir (S)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.

Victoire Pateau (V)

CHRU, Brest, 29200, France.

Julien Bert (J)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.

Jean-François Clement (JF)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.

Hadi Fayad (H)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.
Hamad Medical Corporation OHS, PET/CT center Doha, Doha, Qatar.

Erwan l'Her (E)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.
CHRU, Brest, 29200, France.

Dimitris Visvikis (D)

INSERM, UMR1101, LaTIM, University of Brest, Brest, 29200, France.

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