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Reseach Article

Implementation of Clustering Algorithm for Vital Signals in WMRHM Framework

Published on March 2012 by Dipti D. Patil, Dnyaneshwar A.Rokade, Vijay M. Wadhai
International Conference in Computational Intelligence
Foundation of Computer Science USA
ICCIA - Number 9
March 2012
Authors: Dipti D. Patil, Dnyaneshwar A.Rokade, Vijay M. Wadhai
06cabe7e-6efc-4dc5-bf3e-3d362e9c527f

Dipti D. Patil, Dnyaneshwar A.Rokade, Vijay M. Wadhai . Implementation of Clustering Algorithm for Vital Signals in WMRHM Framework. International Conference in Computational Intelligence. ICCIA, 9 (March 2012), 36-40.

@article{
author = { Dipti D. Patil, Dnyaneshwar A.Rokade, Vijay M. Wadhai },
title = { Implementation of Clustering Algorithm for Vital Signals in WMRHM Framework },
journal = { International Conference in Computational Intelligence },
issue_date = { March 2012 },
volume = { ICCIA },
number = { 9 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 36-40 },
numpages = 5,
url = { /proceedings/iccia/number9/5154-1066/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Computational Intelligence
%A Dipti D. Patil
%A Dnyaneshwar A.Rokade
%A Vijay M. Wadhai
%T Implementation of Clustering Algorithm for Vital Signals in WMRHM Framework
%J International Conference in Computational Intelligence
%@ 0975-8887
%V ICCIA
%N 9
%P 36-40
%D 2012
%I International Journal of Computer Applications
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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Index Terms

Computer Science
Information Sciences

Keywords

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