Abstract
Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The reliability module in our implementation limits the cross-reliability \(r_{cro}\) to a value between 0 to 1, although theoretically, a coefficient less than 1 can lead to a \(r_{cro}\) higher that 1.
References
WHO: Chronic diseases and health promotion. http://www.who.int/chp/en/. Accessed June 2017
Kyriacos, U.: Monitoring vital signs using early warning scoring systems: a review of the literature. J. Nurs. Manag. 19(3), 311–330 (2011)
Morgan, R.J.M.: An early warning scoring system for detecting developing critical illness. Clin. Intensive Care 8(2), 100 (1997)
Anzanpour, A., et al.: Internet of Things enabled in-home health monitoring system using early warning score. In: Proceedings of MobiHealth (2015)
Anzanpour, A., et al.: Self-awareness in remote health monitoring systems using wearable electronics. In: DATE Conference (2017)
Götzinger, M., Taherinejad, N., Rahmani, A.M., Liljeberg, P., Jantsch, A., Tenhunen, H.: Enhancing the early warning score system using data confidence. In: Perego, P., Andreoni, G., Rizzo, G. (eds.) MobiHealth 2016. LNICST, vol. 192, pp. 91–99. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-58877-3_12
TaheriNejad, N., et al.: Comprehensive observation and its role in self-awareness; an emotion recognition system example. In: Proceedings of FedCSIS (2016)
Pasquier, M., et al.: Cooling rate of 9.4 \(^\circ \)C in an hour in an avalanche victim. Resuscitation 93, e17–e18 (2015)
Reule, S.: Heart rate and blood pressure: any possible implications for management of hypertension? Curr. Hypertens. Rep. 14(6), 478–484 (2012)
Davies, P.: The relationship between body temperature, heart rate and respiratory rate in children. Emerg. Med. J. 26(9), 641–643 (2009)
Zila, I., Calkovska, A.: Effects of elevated body temperature on control of breathing. Acta Medica Martiniana 2011(Supp 1), 24–30 (2011)
Ross, T.J.: Fuzzy Logic with Engineering Applications. Wiley, New York (2009)
Guang, L.: Hierarchical agent monitoring design approach towards self-aware parallel soc. ACM Trans. Embed. Comput. Syst. 9(3), 25 (2010)
Zephyr: Bioharness 3. www.zephyranywhere.com. Accessed June 2017
iHealth: iHealth BP5. www.ihealthlabs.com/blood-pressure-monitors/feel/. Accessed June 2017
iHealth: iHealth PO3. www.ihealthlabs.com/fitness-devices/wireless-pulse-oximeter/. Accessed June 2017
Maxim Integrated: DS18b20. www.maximintegrated.com/en/products/analog/sensors-and-sensor-interface/DS18B20.html. Accessed June 2017
ATMEL: Atmega328p. www.atmel.com/devices/atmega328p. Accessed June 2017
Nordic Semiconductor: nrf51822. www.nordicsemi.com/eng/Products/Bluetooth-low-energy/nRF51822. Accessed June 2017
Song, H.S., et al.: The effects of specific respiratory rates on heart rate and heart rate variability. Appl. Psychophysiol. Biofeedback 28(1), 13–23 (2003)
Acknowledgement
The authors wish to acknowledge the financial support by the Marie Curie Actions of the European Union’s H2020 Programme.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Götzinger, M., Anzanpour, A., Azimi, I., TaheriNejad, N., Rahmani, A.M. (2018). Enhancing the Self-Aware Early Warning Score System Through Fuzzified Data Reliability Assessment. In: Perego, P., Rahmani, A., TaheriNejad, N. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-98551-0_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-98551-0_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-98550-3
Online ISBN: 978-3-319-98551-0
eBook Packages: Computer ScienceComputer Science (R0)