Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Vital signals like ECG, EEG, SpO2, BP etc. can be monitor through wireless sensor networks and analyzed with the help of data mining techniques. These real-time signals are continuous in nature and abruptly changing hence there is a need to apply an efficient and concept adapting real-time data stream mining techniques for taking intelligent health care decisions online. Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable. The most important criteria is to solve the realtime data streams mining problem with ‘concept drift’ efficiently. This paper presents the state-of-the art in this field with growing vitality and introduces the methods for detecting concept drift in data stream, then gives a significant summary of existing approaches to the problem of concept drift. The work is focused on applying these real time stream mining algorithms on vital signals of human body in health care environment

1. P. Domingos and G. Hulten. Mining high-speed data streams. In Knowledge Discovery and Data Mining, pages 71–80, 2000.
2. M. B. Harries, C. Sammut, and K. Horn. Extracting hidden context. Machine Learning, 32(2):101–126, 1998.
3. G. Hulten, L. Spencer, and P. Domingos. Mining Time changing data streams.
4. Dengyuan Wu1,2,4, Ying Liu1,5, Ge Gao3 Zhendong Mao1,2,4, Weishan Ma1,2,4, Tao He2,4 “ AN ADAPTIVE ENSEMBLE CLASSIFIER FOR CONCEPT DRIFTING STREAM” 1Gaduate University of Chinese Academy of Sciences 2 Institute of Computing Technology, CAS 3University of Virginia 4 Beijing Zhongke Fulong Computer Technology Co., Ltd. 5. Fictitious Economy and Data Science Research Center, CAS Dengyuan Wu; Ying Liu; Ge Gao; Zhendong Mao; Weishan Ma; Tao He; Computational Intelligence and Data Mining, 2009. CIDM '09. IEEE Symposium
5. Leandro L. Minku, Student Member, IEEE, Allan P. White, and Xin Yao, Fellow,”The impact of diversity on learning in the presence of concept drift” IEEE Transactions on Knowledge and Data Engineering 2010
6. Micheline Kamber ” Data Mining: Concepts and Techniques“Second Edition Jiawei Han University of Illinois at Urbana-Champaign
7. Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer “ DATA STREAM MINING A Practical Approach” ( Aug 2009)
8. Qun Zhu1, Xuegang Hu1, Yuhong Zhang1, Peipei Li1, Xindong Wu1,2 School of Computer Science and Information Engineering, Hefei University of Technology, China, 230009 “ A Double-Window-based Classification Algorithm for Concept Drifting Data Streams” Department of Computer Science, University of Vermont, USA, 05405(2010)
9. Indre _Zliobait_e “Learning under Concept Drift: an Overview” Faculty of Mathematics and Informatics Vilnius University, Lithuania (22 oct 2010)
10. ] The MIMIC database on PhysioBank (2007, Oct.) [Online]. Available: http://www.physionet.org/physiobank/database/mimicdb
11. Daniele Apiletti, Elena Baralis, Member, IEEE, Giulia Bruno, and Tania Cerquitelli “Real time Analysis of Physiological Data to Support Medical Applications” (2009 May)

Real-time data stream mining concept-drift vital Signal processing Health Care