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Implementation of Clustering Algorithm for Vital Signals in WMRHM Framework

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Author(s): Dipti D. Patil | Dnyaneshwar A.Rokade | Vijay M. Wadhai

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: iccia;
Issue: 9;
Date: 2012;
Original page

Keywords: Real time data stream mining | K-means | Graph Theoretic | Vital signal processing in WMRHM

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 realtime 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. 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
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