Concept Adapting Real-Time Data Stream Mining for Health Care Applications
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 Because of the high speed and huge volume data set in data streams, the traditional classification technologies are no longer applicable.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 are to solve the real-time 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.
KeywordsReal-time data stream mining concept-drift vital Signal processing Health Care
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