{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:59:32Z","timestamp":1775577572730,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u201335 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.<\/jats:p>","DOI":"10.3390\/s20051360","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"1360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Sleep Apnea Detection with Polysomnography and Depth Sensors"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0931-4017","authenticated-orcid":false,"given":"Martin","family":"Sch\u00e4tz","sequence":"first","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0270-1738","authenticated-orcid":false,"given":"Ale\u0161","family":"Proch\u00e1zka","sequence":"additional","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic"},{"name":"Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 160 00 Prague 6, Czech Republic"},{"name":"University Hospital Hradec Kr\u00e1lov\u00e9, Faculty of Medicine in Hradec Kr\u00e1lov\u00e9, Department of Neurology, Charles University, 500 05 Hradec Kr\u00e1lov\u00e9, Czech Republic"}]},{"given":"Ji\u0159\u00ed","family":"Kuchy\u0148ka","sequence":"additional","affiliation":[{"name":"University Hospital Hradec Kr\u00e1lov\u00e9, Faculty of Medicine in Hradec Kr\u00e1lov\u00e9, Department of Neurology, Charles University, 500 05 Hradec Kr\u00e1lov\u00e9, Czech Republic"}]},{"given":"Old\u0159ich","family":"Vy\u0161ata","sequence":"additional","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague 6, Czech Republic"},{"name":"University Hospital Hradec Kr\u00e1lov\u00e9, Faculty of Medicine in Hradec Kr\u00e1lov\u00e9, Department of Neurology, Charles University, 500 05 Hradec Kr\u00e1lov\u00e9, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sch\u00e4tz, M., Centonze, F., Kuchynka, J., Tupa, O., Vysata, O., Geman, O., and Prochazka, A. (2015, January 29\u201330). Statistical recognition of breathing by MS Kinect depth sensor. Proceedings of the 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Prague, Czech Republic.","DOI":"10.1109\/IWCIM.2015.7347062"},{"key":"ref_2","first-page":"1","article-title":"Motion tracking and gait feature estimation for recognising Parkinson\u2019s disease using MS Kinect","volume":"14","author":"Schatz","year":"2015","journal-title":"BioMed. Eng. OnLine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.dsp.2015.05.011","article-title":"Bayesian Classification and Analysis of Gait Disorders Using Image and Depth Sensors of Microsoft Kinect","volume":"47","author":"Schatz","year":"2015","journal-title":"Elsevier Digit. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s00521-015-1827-x","article-title":"Use of Image and Depth Sensors of the Microsoft Kinect for the Detection of Gait Disorders","volume":"26","author":"Schatz","year":"2015","journal-title":"Springer Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"13070","DOI":"10.3390\/rs71013070","article-title":"Assessment and Calibration of a RGB-D Camera (Kinect v2 Sensor) Towards a Potential Use for Close-Range 3D Modeling","volume":"7","author":"Lachat","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"24297","DOI":"10.3390\/s150924297","article-title":"Leveraging Two Kinect Sensors for Accurate Full-Body Motion Capture","volume":"15","author":"Gao","year":"2015","journal-title":"Sensors"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Addison, P.S., Smit, P., Jacquel, D., and Borg, U.R. (2019). Continuous respiratory rate monitoring during an acute hypoxic challenge using a depth sensing camera. J. Clin. Monit. Comput., 1\u20139.","DOI":"10.1007\/s10877-019-00417-6"},{"key":"ref_8","unstructured":"Carey, G. (2020, March 02). How Intel\u2019s RealSense Has Come of Age | Digital Trends. Available online: https:\/\/www.digitaltrends.com\/computing\/intel-realsense-coming-of-age\/."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Martinez, M., and Stiefelhagen, R. (2017, January 24\u201331). Breathing rate monitoring during sleep from a depth camera under real-life conditions. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.135"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1109\/TMM.2016.2626969","article-title":"Sleep Apnea Detection via Depth Video and Audio Feature Learning","volume":"19","author":"Yang","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Proch\u00e1zka, A., Charv\u00e1tov\u00e1, H., Vy\u0161ata, O., Kopal, J., and Chambers, J. (2017). Breathing Analysis Using Thermal and Depth Imaging Camera Video Records. Sensors, 17.","DOI":"10.3390\/s17061408"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, Y.K., Chen, H.Y., and Chen, J.R. (2019). Unobtrusive Sleep Monitoring Using Movement Activity by Video Analysis. Electronics, 8.","DOI":"10.3390\/electronics8070812"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"A121","DOI":"10.1136\/thoraxjnl-2011-201054c.134","article-title":"P134 Validating structured light plethysmography (SLP) as a non-invasive method of measuring lung function when compared to Spirometry","volume":"66","author":"Alimohamed","year":"2011","journal-title":"Thorax"},{"key":"ref_14","first-page":"A2528","article-title":"Tidal Breathing Parameters Measurement by Structured Light Plethysmography (SLP) and Spirometry","volume":"B18","author":"Brand","year":"2010","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1109\/TBME.2013.2280132","article-title":"Unconstrained video monitoring of breathing behavior and application to diagnosis of sleep apnea","volume":"61","author":"Wang","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MEMB.2006.1636352","article-title":"Noncontact measurement of breathing function","volume":"25","author":"Murthy","year":"2014","journal-title":"IEEE Eng. Med. Biol. Mag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6383","DOI":"10.3390\/s150306383","article-title":"Assessment of Human Respiration Patterns via Noncontact Sensing Using Doppler Multi-Radar System","volume":"15","author":"Gu","year":"2015","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15371","DOI":"10.3390\/s140815371","article-title":"An Ultrasonic Contactless Sensor for Breathing Monitoring","volume":"14","author":"Arlotto","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18950","DOI":"10.3390\/s150818950","article-title":"Monitoring of Weekly Sleep Pattern Variations at Home with a Contactless Biomotion Sensor","volume":"15","author":"Hashizaki","year":"2014","journal-title":"Sensors"},{"key":"ref_20","first-page":"11","article-title":"Force Sensitive Resistance Based Heart Beat Monitoring For Health Care System","volume":"4","author":"Pandiyan","year":"2014","journal-title":"Int. J. Inf. Sci. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nam, Y., Kim, Y., and Lee, J. (2016). Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor. Sensors, 16.","DOI":"10.3390\/s16050750"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/4351435","article-title":"A Human Activity Recognition System Using Skeleton Data from RGBD Sensors","volume":"2016","author":"Cippitelli","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MSP.2015.2489978","article-title":"Sensors in Assisted Living","volume":"33","author":"Erden","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_24","first-page":"1","article-title":"A depth camera motion analysis framework for tele-rehabilitation: Motion capture and person-centric kinematics analysis","volume":"2016","author":"Ye","year":"2016","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Rolls, E., and Deco, G. (2001). Computational Neuroscience of Vision, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780198524885.001.0001"},{"key":"ref_26","unstructured":"Salatas, J. (2020, March 01). Implementation of Competitive Learning Networks for WEKA - ICT Research Blog. Available online: https:\/\/jsalatas.ictpro.gr\/implementation-of-competitive-learning-networks-for-weka\/."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1090\/S0025-5718-1978-0468306-4","article-title":"On computing the discrete Fourier transform","volume":"32","author":"Winograd","year":"1978","journal-title":"Math. Comput."},{"key":"ref_28","first-page":"325","article-title":"Clinical and physiologic heterogeneity of the central sleep apnea syndrome","volume":"305","author":"Bradley","year":"1981","journal-title":"Am. Rev. Respir. Dis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1126\/science.181.4102.856","article-title":"Insomnia with sleep apnea: A new syndrome","volume":"181","author":"Guilleminault","year":"1973","journal-title":"Science"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1002\/ana.410210509","article-title":"Central sleep apnea and partial obstruction of the upper airway","volume":"21","author":"Guilleminault","year":"1987","journal-title":"Ann. Neurol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1378\/chest.90.2.165","article-title":"Reversal of central sleep apnea using nasal CPAP","volume":"90","author":"Issa","year":"1986","journal-title":"Chest"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1056\/NEJM198108063050606","article-title":"Respiratory dysrhythmias during sleep","volume":"305","author":"Cherniack","year":"1981","journal-title":"N. Engl. J. Med."},{"key":"ref_33","unstructured":"Thorpy, M.J., and Rochester, M. (1997). International Classification of Sleep Disorders: Diagnostic and Coding Manual, Revised, American Academy of Sleep Medicine."},{"key":"ref_34","unstructured":"Sch\u00e4tz, M., Kuchy\u0148ka, J., Vy\u0161ata, O., and Proch\u00e1zka, A. (2020, March 01). Pilot Study of Sleep Apnea Detection with Wavelet Transform; Technical Computing Prague; 2017. Available online: https:\/\/pdfs.semanticscholar.org\/3a1e\/893519193746565bc2559d5181c29c95e422.pdf."},{"key":"ref_35","unstructured":"Deboer, S.L. (2006). Emergency Newborn Care, Trafford."},{"key":"ref_36","unstructured":"Lindh, W., Pooler, M., Tamparo, C., and Dahl, B. (2009). Delmar\u2019s Comprehensive Medical Assisting: Administrative and Clinical Competencies, Cengage Learning."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lee, Y.S., Pathirana, P.N., Member, S., and Steinfort, C.L. (2014, January 8\u201310). Respiration Rate and Breathing Patterns from Doppler Radar Measurements. Proceedings of the 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES), Kuala Lumpur, Malaysia.","DOI":"10.1109\/IECBES.2014.7047493"},{"key":"ref_38","unstructured":"Barrett, K.E., Barman, S.M., Boitano, S., and Brooks, H. (2012). Ganong\u2019s Review of Medical Physiology, McGraw-Hill Education. [24th ed.]."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2238","DOI":"10.1111\/jgs.12580","article-title":"Normal respiratory rate and peripheral blood oxygen saturation in the elderly population","volume":"61","author":"Narvaiza","year":"2013","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wasenm\u00fcller, O., and Stricker, D. (2017). Comparison of Kinect V1 and V2 Depth Images in Terms of Accuracy and Precision, Springer.","DOI":"10.1007\/978-3-319-54427-4_3"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Keselman, L., Woodfill, J.I., Grunnet-Jepsen, A., and Bhowmik, A. (2017). Intel RealSense Stereoscopic Depth Cameras. arXiv.","DOI":"10.1109\/CVPRW.2017.167"},{"key":"ref_42","unstructured":"Intel (2016). Intel \u00ae RealSense\u2122 Camera R200 Embedded Infrared Assisted Stereovision 3D Imaging System with Color Camera Product Datasheet R200, Available online: https:\/\/www.mouser.com\/pdfdocs\/intel_realsense_camera_r200.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1360\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:03:13Z","timestamp":1760173393000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/5\/1360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,2]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["s20051360"],"URL":"https:\/\/doi.org\/10.3390\/s20051360","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,2]]}}}