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Identification of Patient Deterioration in Vital-Sign Data using One-Class Support Vector Machines., , , and . FedCSIS, page 125-131. (2011)Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study., , , , and . BMC Medical Informatics Decis. Mak., (2015)Probabilistic Novelty Detection With Support Vector Machines., , , , , and . IEEE Trans. Reliab., 63 (2): 455-467 (2014)Gaussian process regression in vital-sign early warning systems., , , , and . EMBC, page 6161-6164. IEEE, (2012)Visualisation of long-term ECG signals applied to post-intensive care patients., , , and . BSN, page 165-168. IEEE, (2018)"Stroke Nav": A wireless data collection and review system to support stroke care delivery., , , , and . Comput. Methods Programs Biomed., 108 (1): 338-345 (2012)Probabilistic Patient Monitoring using Extreme Value Theory - A Multivariate, Multimodal Methodology for Detecting Patient Deterioration., , and . BIOSIGNALS, page 5-12. INSTICC Press, (2010)An impedance pneumography signal quality index: Design, assessment and application to respiratory rate monitoring., , , , , , and . Biomed. Signal Process. Control., (2021)Non-contact physiological monitoring of preterm infants in the Neonatal Intensive Care Unit., , , , , , , , , and . npj Digit. Medicine, (2019)Extending the Generalised Pareto Distribution for Novelty Detection in High-Dimensional Spaces., , , and . J. Signal Process. Syst., 74 (3): 323-339 (2014)