Commenced in January 2007
Paper Count: 31108
Processing the Medical Sensors Signals Using Fuzzy Inference System
Abstract:Sensors possess several properties of physical measures. Whether devices that convert a sensed signal into an electrical signal, chemical sensors and biosensors, thus all these sensors can be considered as an interface between the physical and electrical equipment. The problem is the analysis of the multitudes of saved settings as input variables. However, they do not all have the same level of influence on the outputs. In order to identify the most sensitive parameters, those that can guide users in gathering information on the ground and in the process of model calibration and sensitivity analysis for the effect of each change made. Mathematical models used for processing become very complex. In this paper a fuzzy rule-based system is proposed as a solution for this problem. The system collects the available signals information from sensors. Moreover, the system allows the study of the influence of the various factors that take part in the decision system. Since its inception fuzzy set theory has been regarded as a formalism suitable to deal with the imprecision intrinsic to many problems. At the same time, fuzzy sets allow to use symbolic models. In this study an example was applied for resolving variety of physiological parameters that define human health state. The application system was done for medical diagnosis help. The inputs are the signals expressed the cardiovascular system parameters, blood pressure, Respiratory system paramsystem was done, it will be able to predict the state of patient according any input values.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1329561Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1679
 Zadeh L.A. Fuzzy logic to extended fuzzy logic- The concept of fvalidity and the impossibility principle. Fuzzy-IEEE, Imperial college London, UK. 2007
 Agency for Healthcare Research and Quality. Making health care safer: a critical analysis of patient safety practices. Rockville, MD: AHRQ Publications; 2001.
 Dr├Àg┼½nas K., Povilionis E. Cardiac Output and Homodynamic Monitoring System "Heartlab" // Proceedings of International Conference on Biomedical Engineering. - Kaunas: Technologija,. - P. 100-105. 1999
 Miyasaka K, Kondo Y, Suzuki T, Sakai H, Takata M. Toward better home respiratory monitoring: a comparison of inductance and impedance pneumography // Acta Paediatr Japonic.. Vol. 36(3). P. 307- 310. 1994
 Warren R.H., Horan S.M., Robertson P.K. Chest wall motion in preterm infants using respiratory inductive plethysmography // European Respiratory Journal. October,. Vol. 10(10). P. 295-300. 1997
 Silvia Alay├│n ; Richard Robertson ; Simon K. Warfield, and Juan Ruiz- Alzola. A Fuzzy System for Helping Medical Diagnosis of Malformations of Cortical Development J Biomed Inform. June ; 40(3): 221-235. 2007
 Pedrycz, W..Fuzzy modelling: paradigms and practice. Kluwer Academic Press; 1996
 Driankov, D.; Hellendoorn, H.; Reinfrank, M. An introduction to fuzzy control.Springer-Verlag; 1993
 Chi, Z.; Yan, H.; Pham, T. Fuzzy algorithms: with applications to image processing and pattern recognition. World Scientific; 1996.
 Bouharati S.; Benmahammed K.; Harzallah D. and El-Assaf Y.M. Application of artificial neuro-fuzzy logic inference system for predicting the microbiological pollution in fresh water. Journal of Applied Sciences 8(2): 309-315., 2008
 Demir F, Korkmaz KA. Prediction of lower and upper bounds of elastic modulus of high strength concrete. Constr Build Mater. 22(7):1385-93., 2008
 Mamdani, E. H. Application of the fuzzy logic to approximate reasoning using linguistic synthesis. IEEE Transactions and Computers, C-26, 1182-1191., 1977
 Zadeh L. Fuzzy sets. Information and Control 1; 8:338-353. 1965