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A NOVEL CONTINUOUS BLOOD PRESSURE ESTIMATION APPROACH BASED ON DATA MINING TECHNIQUES

A NOVEL CONTINUOUS BLOOD PRESSURE ESTIMATION APPROACH BASED ON DATA MINING TECHNIQUES

ABSTRACT

                             Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for un obtrusive BP measurement novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism-driven model. First, 14features derived from simultaneous electrocardiogram and photo plethysmogram signals were extracted for beat-to-beat BP estimation A genetic algorithm-based feature selection method was then used to select BP indicators for each subject Experimental results based on 73 subjects showed that the proposed approach exhibited excellent accuracy in static BP estimation

EXISTING SYSTEM

                   Continuous blood pressure (BP) estimation using pulse transit time (PTT) is a promising method for unobtrusive BP measurement. However, the accuracy of this approach must be improved for it to be viable for a wide range of applications.

DISADVANTAGES
 Accuracy is very low
 relatively stable
PROPOSED SYSTEM
                   Novel continuous BP estimation approach that combines data mining techniques with a traditional mechanism driven model. First, 14features derived from simultaneous electrocardiogram and photo plethysmogram signals were extracted for beat-to-beat BP estimation A genetic algorithm-based feature selection method was then used to select BP indicators for each subject.

ADVANTAGES
 Increase the accuracy
 Standard deviation in different intervals
 Robustness of the model

REFERENCES
 A. Vchobanian, G. Lbakris, H. Rblack, "The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: The JNC 7 Report, " JAMA,vol. 289, no. 19, 2003.
 N. Zakopoulos, G. Tsivgoulis, G. Barlas, "Time Rate of Blood Pressure Variation Is Associated With Increased Common Carotid Artery Intimae-Media Thickness, " Hypertension,vol. 45, no. 4, pp. 505-512, 2005.
 P. Palatini, G. Reboldi, L. Jbeilin, "Added Predictive Value of Night-Time Blood Pressure Variability for Cardiovascur Events and Mortality The Ambulatory Blood Pressure International Study, " Hypertension, vol. 64, no.3, pp. 487-493, 2014.
 Y. T. Zhang, Y. L. Zheng, W. H. Lin, H. Y. Zhang and X. L. Zhou, "Challenges and Opportunities in Cardiovascular Health Informatics," in IEEE Transactions on Biomedical Engineering, vol. 60, no. 3, pp. 633-642, March 2013.


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