NDLI: Dynamic data mining approach to WMRHM

Content Provider	IEEE Xplore Digital Library
Author	Patil, D.D. Wadhai, V.M.
Copyright Year	2012
Description	Author affiliation: MAEER's MITCOE, Pune, India (Wadhai, V.M.) \|\| Dept. Computer Engineering, MAEER's MITCOE, Pune, India (Patil, D.D.)
Abstract	Developments in sensors, miniaturization of low-power microelectronics, and wireless networks are becoming a significant opportunity for improving the quality of health care services. Since the population is growing, the need for high quality and efficient healthcare, both at home and in hospital, is becoming more important. This paper presents the innovative wireless sensor network based Mobile Real-time Health care Monitoring (WMRHM) framework which has the capacity of giving health predictions online based on continuously monitored real time vital body signals. Our approach focused towards handling all kinds of vital signals like ECG, EMG, SpO2 etc. which previous work was not supporting. While predictions the framework considers all parameters like patient history, domain expert's rules and continuously monitored real-time signals. Implementation and results of applying clustering algorithms (Graph theoretic, K-means) on patient's historical health data for forming the health rule base are discussed here. The framework has been designed to perform the analysis on the instantaneous and stream (continuous) data over a sliding time window which applies dynamic data mining on the live data. The comparative analysis on vital signals made from various clustering algorithms adds extra dimension to global risk alerts and help doctors to diagnose more accurately.
Starting Page	1978
Ending Page	1983
File Size	521491
Page Count	6
File Format	PDF
ISBN	9781457721182
e-ISBN	9781457721199
DOI	10.1109/ICIEA.2012.6361053
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering algorithms Real-time systems Biomedical monitoring Data mining Monitoring Medical services Heuristic algorithms Wireless Sensor Networks Dynamic data mining Clustering vital signal Processing
Content Type	Text
Resource Type	Article

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