{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T17:11:56Z","timestamp":1746551516178,"version":"3.37.3"},"reference-count":28,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T00:00:00Z","timestamp":1605225600000},"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":"Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing analysis system that automatically detects different types of breathing patterns of clinical significance. Accelerometer and gyroscopic data were collected from light-weight wireless sensors placed on the chest and abdomen of 100 normal volunteers who simulated various breathing events (central sleep apnea, coughing, obstructive sleep apnea, sighing, and yawning). We then constructed synthetic datasets by injecting annotated examples of the various patterns into segments of normal breathing. A one-dimensional convolutional neural network was implemented to detect the location of each event in each synthetic dataset and to classify it as belonging to one of the above event types. We achieved a mean F1 score of 92% for normal breathing, 87% for central sleep apnea, 72% for coughing, 51% for obstructive sleep apnea, 57% for sighing, and 63% for yawning. These results demonstrate that using deep learning to analyze chest and abdomen movement data from wearable sensors provides an unobtrusive means of monitoring the breathing pattern. This could have application in a number of critical medical situations such as detecting apneas during sleep at home and monitoring breathing events in mechanically ventilated patients in the intensive care unit.<\/jats:p>","DOI":"10.3390\/s20226481","type":"journal-article","created":{"date-parts":[[2020,11,13]],"date-time":"2020-11-13T13:44:02Z","timestamp":1605275042000},"page":"6481","source":"Crossref","is-referenced-by-count":36,"title":["Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning"],"prefix":"10.3390","volume":"20","author":[{"given":"Kristin","family":"McClure","sequence":"first","affiliation":[{"name":"College of Engineering and Mathematical Sciences, The University of Vermont, Burlington, VT 05405, USA"}]},{"given":"Brett","family":"Erdreich","sequence":"additional","affiliation":[{"name":"Department of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA"}]},{"given":"Jason H. T.","family":"Bates","sequence":"additional","affiliation":[{"name":"Department of Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8396-6967","authenticated-orcid":false,"given":"Ryan S.","family":"McGinnis","sequence":"additional","affiliation":[{"name":"College of Engineering and Mathematical Sciences, The University of Vermont, Burlington, VT 05405, USA"}]},{"given":"Axel","family":"Masquelin","sequence":"additional","affiliation":[{"name":"College of Engineering and Mathematical Sciences, The University of Vermont, Burlington, VT 05405, USA"}]},{"given":"Safwan","family":"Wshah","sequence":"additional","affiliation":[{"name":"College of Engineering and Mathematical Sciences, The University of Vermont, Burlington, VT 05405, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1097\/ACO.0000000000000265","article-title":"Ob-structive sleep apnea, pain, and opioids: Is the riddle solved?","volume":"29","author":"Lam","year":"2016","journal-title":"Curr. Opin. Anaesthesiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2017\/9109054","article-title":"Abnormal Breathing Patterns Predict Extubation Failure in Neurocritically Ill Patients","volume":"2017","author":"Punj","year":"2017","journal-title":"Case Rep. Crit. Care"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dias, D., and Cunha, J.P.S. (2018). Wearable Health Devices\u2014Vital Sign Monitoring, Systems and Technologies. Sensors, 18.","DOI":"10.3390\/s18082414"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1109\/TBCAS.2018.2824659","article-title":"Online Obstructive Sleep Apnea Detection on Medical Wearable Sensors","volume":"12","author":"Surrel","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/JBHI.2017.2740120","article-title":"Cardiores-piratory model-based data-driven approach for sleep apnea detection","volume":"22","author":"Gutta","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4981","DOI":"10.1109\/JSEN.2018.2828599","article-title":"Accelerometry-Based Estimation of Respiratory Rate for Post-Intensive Care Patient Monitoring","volume":"18","author":"Jarchi","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_7","first-page":"1555","article-title":"mHealth tools for monitoring obstructive sleep apnea patients at home: Proof-of-concept","volume":"217","author":"Camara","year":"2017","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1513\/pats.200708-137MG","article-title":"Obesity and obstructive sleep apnea: Pathogenic mechanisms and therapeutic approaches","volume":"15","author":"Schwartz","year":"2008","journal-title":"Proc. Am. Thorac. Soc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.ccm.2010.02.005","article-title":"Polysomnography","volume":"31","author":"Jafari","year":"2010","journal-title":"Clin. Chest Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/1902176","article-title":"A Novel Sleep Respiratory Rate Detection Method for Obstructive Sleep Apnea Based on Characteristic Moment Waveform","volume":"2018","author":"Fang","year":"2018","journal-title":"J. Health Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.bios.2018.05.038","article-title":"Wearable humidity sensor based on porous graphene network for respiration monitoring","volume":"116","author":"Pang","year":"2018","journal-title":"Biosens. Bioelectron."},{"key":"ref_12","first-page":"2865","article-title":"Wearable textile based on silver plated knitted sensor for respiratory rate monitoring","volume":"2018","author":"Molinaro","year":"2018","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1164\/ajrccm.157.1.9706079","article-title":"Prevalence of sleep-disordered breathing in women: Effects of gender","volume":"157","author":"Bixler","year":"1998","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"ref_14","unstructured":"Clinic, M. (2019, October 01). Sleep Apnea. Available online: https:\/\/www.mayoclinic.org\/diseases-conditions\/sleep-apnea\/symptoms-causes\/syc-20377631."},{"key":"ref_15","unstructured":"(1999). Sleep-related breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research. Sleep, 667\u2013689."},{"key":"ref_16","unstructured":"W3C (2019, October 01). Motion Sensors Explainer. Available online: https:\/\/www.w3.org\/TR\/motion-sensors\/."},{"key":"ref_17","unstructured":"Chollet, F. (2019, October 01). Keras. Available online: https:\/\/github.com\/fchollet\/keras."},{"key":"ref_18","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_20","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_22","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large kernel matters\u2014Improve semantic segmentation by global convolutional network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A deep learning approach to human activity recognition based on single accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Robles-Rubio, C.A., Kearney, R.E., Bertolizio, G., and Brown, K. (2020). Automatic unsupervised respiratory analysis of infant respiratory inductance plethysmography signals. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238402"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"074001","DOI":"10.1088\/1361-6579\/ab2664","article-title":"Hybrid scattering-LSTM networks for automated detection of sleep arousals","volume":"40","author":"Warrick","year":"2019","journal-title":"Physiol. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6481\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,5]],"date-time":"2024-07-05T07:37:45Z","timestamp":1720165065000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/22\/6481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,13]]},"references-count":28,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20226481"],"URL":"https:\/\/doi.org\/10.3390\/s20226481","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,11,13]]}}}