Abstract
In this paper, we propose a new health monitoring system (HMS) based on a new classification method consisting of the real time support vector machines (RTSVM). The new HMS denoted by RTSVM-MS deals with problems of monitoring systems in intensive care unit (ICU). The main aim of this new system is to considerably reduce the rate of false alarms and keep a high and stable level of sensitivity. Besides, it overcomes the main issue of the existing HMS by proposing a classification model that considers the variation of the patient states over time. In addition, the thresholds set has to be modified when patients are getting better. However, thresholds are stable and do not translate the states of patients over time since, all existing systems in ICU do not take into account of the patients’ states evolution. Our proposal has the ability to generate an initial model that classifies states of patients to normal and abnormal (critical) using the LASVM. Then, it updates its model by considering the evolution in the states of patients using RTSVM. As a result, the new system gives what the medical staff wants as information and alarms relative to monitored patient.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adamski, P.: About the Healthcare Technology Safety Institute. HTSI Webinar Series on Alarm Systems Management, https://www.aami.org/meetings/webinars/2013/092513_HTSI_TJC_NPSG.html (accessed Januray 11, 2014)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units. International Journal of Computer Applications, Foundation of Computer Science 49, 41–47 (2012)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Incremental Support Vector Machines for Monitoring Systems in Intensive Care Unit. In: SAI 2013, pp. 496–501 (2013)
Ben Rejab, F., Nouira, K., Trabelsi, A.: On the use of the incremental support vector machines for monitoring systems in intensive care unit. In: TAEECE 2013, pp. 266–270 (2013)
Ben Rejab, F., Nouira, K., Trabelsi, A.: Health Monitoring Systems Using Machine Learning Techniques. Intelligent Systems for Science and Information, 423–440 (2014)
Bordes, A., Ertekin, S., Weston, J., Bottou, J.: Fast Kernel Classifiers With Online And Active Learning. Journal of Machine Learning Research 6, 1579–1619 (2005)
Borowski, M., Siebig, S., Wrede, C., Imhoff, M.: Reducing false alarms of intensive care online monitoring systems: An evaluation of two signal extraction algorithms. Computational and Mathematical Methods in Medicine, vol. 2011 (2011)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Adv. Neural Information Processing Systems (NIPS 2000), vol. 13, pp. 409–415 (2000)
Chambrin, M., Ravaux, P., Calvelo-Aros, D., Jaborska, A., Chopin, C., Boniface, B.: Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med., 1360–1366 (1999)
Chang, C., Lin, C.: LIBSVM: A library for support vector machines. Transactions on Intelligent Systems and Technology 2, 1–27 (2011)
Charbonnie, S., Gentil, S.: A trend-based alarm system to improve patient monitoring in intensive care units. Control Engineering Practice 15, 1039–1050 (2007)
Cortes, C., Vapnik, V.: Support vector networks. Machine Learning 20, 273–297 (1995)
Cvach, M.: Monitor alarm fatigue: an integrative review. Biomed. Instrum. Technol. 46, 268–277 (2012)
Food and Drug Administration: Alarming Monitor Problems: Preventing Medical Errors. FDA Patient Safety News (January 2011), www.accessdata.fda.gov/scripts/cdrh/cfdocs/psn/transcript.cfm?show=106#7 (accessed July 27, 2011)
Frasconi, P., Passerini, A.: Predicting the geometry of metal binding sites from protein sequence. IEEE/ACM Transactions on Computational Biology and Bioinformatics 9, 203–213 (2012)
Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P., Mark, R., Mietus, J., Moody, G., Peng, C., Stanley, H.: PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals 101, e215–e220 (2000)
Korniewicz, D., Clark, T., David, Y.: A national online survey on the effectiveness of clinical alarms. American Journal of Critical Care, 36–41 (2008)
Nouira, K., Trabelsi, A.: Intelligent monitoring system for intensive care units. Journal of Medical Systems 36, 2309–2318 (2011)
Schmid, F., Goepfert, M.S., Kuhnt, D., Eichhorn, V., Diedrichs, S., Reichenspurner, H., Goetz, A.E., Reuter, D.A.: The wolf is crying in the operating room: patient monitor and anesthesia workstation alarming patterns during cardiac surgery. Anesth Analgesia, 78–83 (2011)
Siebig, S., Kuhls, S., Imhoff, M., Langgartner, J., Reng, M., Scholmerich, J., Gather, U., Wrede, C.E.: Collection of annotated data in a clinical validation study for alarm algorithms in intensive care-a methodologic framework. Journal of Critical Care 25, 128–135 (2010)
Tsien, C.: Reducing False Alarms in the Intensive Care Unit: A Systematic Comparison of Four Algorithms. In: Proceedings of the AMIA Annual Fall Symposium. American Medical Informatics Association (1997)
Wang, Z., Vucetic, S.: Online training on a budget of support vector machines using twin prototypes. Statistical Analysis and Data Mining 3, 149–169 (2010)
Zotev, V., Phillips, R., Yuan, H., Misaki, M., Bodurka, J.: Self-regulation of human brain activity using simultaneous real-time fMRI and EEG neurofeedback. NeuroImage 85, 985–995 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Rejab, F.B., Nouira, K., Trabelsi, A. (2014). Real Time SVM for Health Monitoring System. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-09891-3_28
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09890-6
Online ISBN: 978-3-319-09891-3
eBook Packages: Computer ScienceComputer Science (R0)